diff --git a/notebooks/auc_experiments/03-auc-test-aggregated-not-stratified.ipynb b/notebooks/auc_experiments/01-experiment-aggregated-not-stratified.ipynb
similarity index 75%
rename from notebooks/auc_experiments/03-auc-test-aggregated-not-stratified.ipynb
rename to notebooks/auc_experiments/01-experiment-aggregated-not-stratified.ipynb
index effb15b..8827de3 100644
--- a/notebooks/auc_experiments/03-auc-test-aggregated-not-stratified.ipynb
+++ b/notebooks/auc_experiments/01-experiment-aggregated-not-stratified.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -68,7 +68,7 @@
        "28"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -163,14 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -225,9 +218,9 @@
     "        p = np.sum(y_test)\n",
     "        n = len(y_test) - np.sum(y_test)\n",
     "\n",
-    "        acc = np.round((tp + tn) / (p + n), 4)\n",
-    "        sens = np.round((tp) / (p), 4)\n",
-    "        spec = np.round((tn) / (n), 4)\n",
+    "        acc = (tp + tn) / (p + n)\n",
+    "        sens = (tp) / (p)\n",
+    "        spec = (tn) / (n)\n",
     "\n",
     "        accs.append(acc)\n",
     "        senss.append(sens)\n",
@@ -246,7 +239,7 @@
     "                tp = np.sum((y_pred >= th) & (y_test == 1))\n",
     "                tn = np.sum((y_pred < th) & (y_test == 0))\n",
     "\n",
-    "                tmp_accs.append(np.round((tp + tn) / len(y_test), 4))\n",
+    "                tmp_accs.append((tp + tn) / len(y_test))\n",
     "\n",
     "            #print(th, np.mean(tmp_accs), best_acc)\n",
     "\n",
@@ -264,9 +257,9 @@
     "            p = np.sum(y_test)\n",
     "            n = len(y_test) - np.sum(y_test)\n",
     "\n",
-    "            best_accs.append(np.round((tp + tn) / len(y_test), 4))\n",
-    "            best_senss.append(np.round((tp) / (p), 4))\n",
-    "            best_specs.append(np.round((tn) / (n), 4))\n",
+    "            best_accs.append((tp + tn) / len(y_test))\n",
+    "            best_senss.append((tp) / (p))\n",
+    "            best_specs.append((tn) / (n))\n",
     "\n",
     "        acc = np.mean(accs)\n",
     "        sens = np.mean(senss)\n",
@@ -281,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -291,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -336,16 +329,16 @@
        "      <th>0</th>\n",
        "      <td>bupa</td>\n",
        "      <td>8</td>\n",
-       "      <td>0.640650</td>\n",
-       "      <td>0.621363</td>\n",
-       "      <td>0.664775</td>\n",
+       "      <td>0.640658</td>\n",
+       "      <td>0.621360</td>\n",
+       "      <td>0.664770</td>\n",
        "      <td>0.679417</td>\n",
-       "      <td>0.669725</td>\n",
-       "      <td>0.587788</td>\n",
-       "      <td>0.746887</td>\n",
+       "      <td>0.669728</td>\n",
+       "      <td>0.587794</td>\n",
+       "      <td>0.746882</td>\n",
        "      <td>0.363737</td>\n",
        "      <td>0.473684</td>\n",
-       "      <td>0.669725</td>\n",
+       "      <td>0.669728</td>\n",
        "      <td>145</td>\n",
        "      <td>200</td>\n",
        "    </tr>\n",
@@ -353,16 +346,16 @@
        "      <th>1</th>\n",
        "      <td>SPECTF</td>\n",
        "      <td>2</td>\n",
-       "      <td>0.715350</td>\n",
-       "      <td>0.437650</td>\n",
+       "      <td>0.715352</td>\n",
+       "      <td>0.437666</td>\n",
        "      <td>0.787850</td>\n",
        "      <td>0.612758</td>\n",
-       "      <td>0.794050</td>\n",
+       "      <td>0.794047</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.778117</td>\n",
        "      <td>inf</td>\n",
-       "      <td>0.794050</td>\n",
+       "      <td>0.794047</td>\n",
        "      <td>55</td>\n",
        "      <td>212</td>\n",
        "    </tr>\n",
@@ -370,16 +363,16 @@
        "      <th>2</th>\n",
        "      <td>appendicitis</td>\n",
        "      <td>4</td>\n",
-       "      <td>0.896025</td>\n",
-       "      <td>0.661925</td>\n",
-       "      <td>0.955500</td>\n",
+       "      <td>0.896011</td>\n",
+       "      <td>0.661905</td>\n",
+       "      <td>0.955487</td>\n",
        "      <td>0.857206</td>\n",
-       "      <td>0.896025</td>\n",
-       "      <td>0.661925</td>\n",
-       "      <td>0.955500</td>\n",
+       "      <td>0.896011</td>\n",
+       "      <td>0.661905</td>\n",
+       "      <td>0.955487</td>\n",
        "      <td>0.448709</td>\n",
        "      <td>0.459785</td>\n",
-       "      <td>0.896025</td>\n",
+       "      <td>0.896011</td>\n",
        "      <td>21</td>\n",
        "      <td>85</td>\n",
        "    </tr>\n",
@@ -387,16 +380,16 @@
        "      <th>3</th>\n",
        "      <td>PC1</td>\n",
        "      <td>5</td>\n",
-       "      <td>0.936000</td>\n",
-       "      <td>0.107260</td>\n",
-       "      <td>0.998060</td>\n",
+       "      <td>0.935991</td>\n",
+       "      <td>0.107280</td>\n",
+       "      <td>0.998057</td>\n",
        "      <td>0.597395</td>\n",
-       "      <td>0.936000</td>\n",
-       "      <td>0.107260</td>\n",
-       "      <td>0.998060</td>\n",
+       "      <td>0.935991</td>\n",
+       "      <td>0.107280</td>\n",
+       "      <td>0.998057</td>\n",
        "      <td>0.980891</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.936000</td>\n",
+       "      <td>0.935991</td>\n",
        "      <td>77</td>\n",
        "      <td>1032</td>\n",
        "    </tr>\n",
@@ -404,16 +397,16 @@
        "      <th>4</th>\n",
        "      <td>wisconsin</td>\n",
        "      <td>3</td>\n",
-       "      <td>0.970733</td>\n",
-       "      <td>0.975200</td>\n",
-       "      <td>0.968500</td>\n",
+       "      <td>0.970728</td>\n",
+       "      <td>0.975203</td>\n",
+       "      <td>0.968495</td>\n",
        "      <td>0.991481</td>\n",
-       "      <td>0.970733</td>\n",
-       "      <td>0.983533</td>\n",
-       "      <td>0.964000</td>\n",
+       "      <td>0.970728</td>\n",
+       "      <td>0.983537</td>\n",
+       "      <td>0.963960</td>\n",
        "      <td>0.399658</td>\n",
-       "      <td>0.307874</td>\n",
-       "      <td>0.970733</td>\n",
+       "      <td>0.310782</td>\n",
+       "      <td>0.970728</td>\n",
        "      <td>239</td>\n",
        "      <td>444</td>\n",
        "    </tr>\n",
@@ -423,21 +416,21 @@
       ],
       "text/plain": [
        "        dataset  k       acc      sens      spec       auc  best_acc  \\\n",
-       "0          bupa  8  0.640650  0.621363  0.664775  0.679417  0.669725   \n",
-       "1        SPECTF  2  0.715350  0.437650  0.787850  0.612758  0.794050   \n",
-       "2  appendicitis  4  0.896025  0.661925  0.955500  0.857206  0.896025   \n",
-       "3           PC1  5  0.936000  0.107260  0.998060  0.597395  0.936000   \n",
-       "4     wisconsin  3  0.970733  0.975200  0.968500  0.991481  0.970733   \n",
+       "0          bupa  8  0.640658  0.621360  0.664770  0.679417  0.669728   \n",
+       "1        SPECTF  2  0.715352  0.437666  0.787850  0.612758  0.794047   \n",
+       "2  appendicitis  4  0.896011  0.661905  0.955487  0.857206  0.896011   \n",
+       "3           PC1  5  0.935991  0.107280  0.998057  0.597395  0.935991   \n",
+       "4     wisconsin  3  0.970728  0.975203  0.968495  0.991481  0.970728   \n",
        "\n",
        "   best_sens  best_spec  threshold  best_threshold  best_acc_orig    p     n  \n",
-       "0   0.587788   0.746887   0.363737        0.473684       0.669725  145   200  \n",
-       "1   0.000000   1.000000   0.778117             inf       0.794050   55   212  \n",
-       "2   0.661925   0.955500   0.448709        0.459785       0.896025   21    85  \n",
-       "3   0.107260   0.998060   0.980891        1.000000       0.936000   77  1032  \n",
-       "4   0.983533   0.964000   0.399658        0.307874       0.970733  239   444  "
+       "0   0.587794   0.746882   0.363737        0.473684       0.669728  145   200  \n",
+       "1   0.000000   1.000000   0.778117             inf       0.794047   55   212  \n",
+       "2   0.661905   0.955487   0.448709        0.459785       0.896011   21    85  \n",
+       "3   0.107280   0.998057   0.980891        1.000000       0.935991   77  1032  \n",
+       "4   0.983537   0.963960   0.399658        0.310782       0.970728  239   444  "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -448,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -485,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -510,6 +503,7 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>dataset</th>\n",
+       "      <th>k</th>\n",
        "      <th>acc</th>\n",
        "      <th>sens</th>\n",
        "      <th>spec</th>\n",
@@ -519,95 +513,118 @@
        "      <th>best_spec</th>\n",
        "      <th>threshold</th>\n",
        "      <th>best_threshold</th>\n",
+       "      <th>best_acc_orig</th>\n",
+       "      <th>p</th>\n",
+       "      <th>n</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>abalone9_18</td>\n",
-       "      <td>0.9728</td>\n",
-       "      <td>0.2000</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>0.647183</td>\n",
-       "      <td>0.9728</td>\n",
-       "      <td>0.2000</td>\n",
-       "      <td>1.0000</td>\n",
+       "      <td>bupa</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.640658</td>\n",
+       "      <td>0.621360</td>\n",
+       "      <td>0.664770</td>\n",
+       "      <td>0.679417</td>\n",
+       "      <td>0.669728</td>\n",
+       "      <td>0.587794</td>\n",
+       "      <td>0.746882</td>\n",
        "      <td>0.363737</td>\n",
-       "      <td>0.714286</td>\n",
+       "      <td>0.473684</td>\n",
+       "      <td>0.669728</td>\n",
+       "      <td>145</td>\n",
+       "      <td>200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>appendicitis</td>\n",
-       "      <td>0.8636</td>\n",
-       "      <td>0.5000</td>\n",
-       "      <td>0.9444</td>\n",
-       "      <td>0.597222</td>\n",
-       "      <td>0.8636</td>\n",
-       "      <td>0.5000</td>\n",
-       "      <td>0.9444</td>\n",
-       "      <td>0.089821</td>\n",
+       "      <td>SPECTF</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.715352</td>\n",
+       "      <td>0.437666</td>\n",
+       "      <td>0.787850</td>\n",
+       "      <td>0.612758</td>\n",
+       "      <td>0.794047</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>1.000000</td>\n",
+       "      <td>0.778117</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>0.794047</td>\n",
+       "      <td>55</td>\n",
+       "      <td>212</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>australian</td>\n",
-       "      <td>0.5435</td>\n",
-       "      <td>0.0156</td>\n",
-       "      <td>1.0000</td>\n",
-       "      <td>0.746199</td>\n",
-       "      <td>0.7174</td>\n",
-       "      <td>0.7812</td>\n",
-       "      <td>0.6622</td>\n",
-       "      <td>0.518418</td>\n",
-       "      <td>0.352733</td>\n",
+       "      <td>appendicitis</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.896011</td>\n",
+       "      <td>0.661905</td>\n",
+       "      <td>0.955487</td>\n",
+       "      <td>0.857206</td>\n",
+       "      <td>0.896011</td>\n",
+       "      <td>0.661905</td>\n",
+       "      <td>0.955487</td>\n",
+       "      <td>0.448709</td>\n",
+       "      <td>0.459785</td>\n",
+       "      <td>0.896011</td>\n",
+       "      <td>21</td>\n",
+       "      <td>85</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>bupa</td>\n",
-       "      <td>0.5942</td>\n",
-       "      <td>0.2414</td>\n",
-       "      <td>0.8500</td>\n",
-       "      <td>0.511207</td>\n",
-       "      <td>0.5942</td>\n",
-       "      <td>0.2414</td>\n",
-       "      <td>0.8500</td>\n",
-       "      <td>0.080741</td>\n",
+       "      <td>PC1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.935991</td>\n",
+       "      <td>0.107280</td>\n",
+       "      <td>0.998057</td>\n",
+       "      <td>0.597395</td>\n",
+       "      <td>0.935991</td>\n",
+       "      <td>0.107280</td>\n",
+       "      <td>0.998057</td>\n",
+       "      <td>0.980891</td>\n",
        "      <td>1.000000</td>\n",
+       "      <td>0.935991</td>\n",
+       "      <td>77</td>\n",
+       "      <td>1032</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>CM1</td>\n",
-       "      <td>0.8900</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.9889</td>\n",
-       "      <td>0.725556</td>\n",
-       "      <td>0.8900</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.9889</td>\n",
-       "      <td>0.441309</td>\n",
-       "      <td>0.570937</td>\n",
+       "      <td>wisconsin</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.970728</td>\n",
+       "      <td>0.975203</td>\n",
+       "      <td>0.968495</td>\n",
+       "      <td>0.991481</td>\n",
+       "      <td>0.970728</td>\n",
+       "      <td>0.983537</td>\n",
+       "      <td>0.963960</td>\n",
+       "      <td>0.399658</td>\n",
+       "      <td>0.310782</td>\n",
+       "      <td>0.970728</td>\n",
+       "      <td>239</td>\n",
+       "      <td>444</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "        dataset     acc    sens    spec       auc  best_acc  best_sens  \\\n",
-       "0   abalone9_18  0.9728  0.2000  1.0000  0.647183    0.9728     0.2000   \n",
-       "1  appendicitis  0.8636  0.5000  0.9444  0.597222    0.8636     0.5000   \n",
-       "2    australian  0.5435  0.0156  1.0000  0.746199    0.7174     0.7812   \n",
-       "3          bupa  0.5942  0.2414  0.8500  0.511207    0.5942     0.2414   \n",
-       "4           CM1  0.8900  0.0000  0.9889  0.725556    0.8900     0.0000   \n",
+       "        dataset  k       acc      sens      spec       auc  best_acc  \\\n",
+       "0          bupa  8  0.640658  0.621360  0.664770  0.679417  0.669728   \n",
+       "1        SPECTF  2  0.715352  0.437666  0.787850  0.612758  0.794047   \n",
+       "2  appendicitis  4  0.896011  0.661905  0.955487  0.857206  0.896011   \n",
+       "3           PC1  5  0.935991  0.107280  0.998057  0.597395  0.935991   \n",
+       "4     wisconsin  3  0.970728  0.975203  0.968495  0.991481  0.970728   \n",
        "\n",
-       "   best_spec  threshold  best_threshold  \n",
-       "0     1.0000   0.363737        0.714286  \n",
-       "1     0.9444   0.089821        1.000000  \n",
-       "2     0.6622   0.518418        0.352733  \n",
-       "3     0.8500   0.080741        1.000000  \n",
-       "4     0.9889   0.441309        0.570937  "
+       "   best_sens  best_spec  threshold  best_threshold  best_acc_orig    p     n  \n",
+       "0   0.587794   0.746882   0.363737        0.473684       0.669728  145   200  \n",
+       "1   0.000000   1.000000   0.778117             inf       0.794047   55   212  \n",
+       "2   0.661905   0.955487   0.448709        0.459785       0.896011   21    85  \n",
+       "3   0.107280   0.998057   0.980891        1.000000       0.935991   77  1032  \n",
+       "4   0.983537   0.963960   0.399658        0.310782       0.970728  239   444  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -625,9 +642,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'auc_int'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'auc_int'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwidth\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m-\u001b[39m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m      2\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhalf_width\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwidth\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m      3\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlower\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mupper\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m  10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m  10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m  10363\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m  10364\u001b[0m     func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m  10372\u001b[0m     kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m  10373\u001b[0m )\n\u001b[0;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mapply()\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m    914\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_standard()\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1062\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1063\u001b[0m         results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_generator()\n\u001b[1;32m   1064\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1065\u001b[0m         results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_numba()\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/apply.py:1081\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1078\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m   1079\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[1;32m   1080\u001b[0m         \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[0;32m-> 1081\u001b[0m         results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc(v, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs)\n\u001b[1;32m   1082\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[1;32m   1083\u001b[0m             \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[1;32m   1084\u001b[0m             \u001b[38;5;66;03m#  series_generator will swap out the underlying data\u001b[39;00m\n\u001b[1;32m   1085\u001b[0m             results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "Cell \u001b[0;32mIn[14], line 1\u001b[0m, in \u001b[0;36m<lambda>\u001b[0;34m(row)\u001b[0m\n\u001b[0;32m----> 1\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwidth\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m-\u001b[39m row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauc_int\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m      2\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhalf_width\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwidth\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m      3\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlower\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mupper\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/series.py:1121\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1118\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[1;32m   1120\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[0;32m-> 1121\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_value(key)\n\u001b[1;32m   1123\u001b[0m \u001b[38;5;66;03m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[1;32m   1124\u001b[0m \u001b[38;5;66;03m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[1;32m   1125\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/series.py:1237\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m   1234\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[1;32m   1236\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[0;32m-> 1237\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mget_loc(label)\n\u001b[1;32m   1239\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(loc):\n\u001b[1;32m   1240\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[loc]\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3810\u001b[0m     ):\n\u001b[1;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'auc_int'"
+     ]
+    }
+   ],
    "source": [
     "data['width'] = data.apply(lambda row: None if row['auc_int'] is None else row['auc_int'][1] - row['auc_int'][0], axis=1)\n",
     "data['half_width'] = data['width'] / 2\n",
diff --git a/notebooks/auc_experiments/03-auc-test-aggregated.ipynb b/notebooks/auc_experiments/01-experiment-aggregated.ipynb
similarity index 99%
rename from notebooks/auc_experiments/03-auc-test-aggregated.ipynb
rename to notebooks/auc_experiments/01-experiment-aggregated.ipynb
index f378b20..722b9d0 100644
--- a/notebooks/auc_experiments/03-auc-test-aggregated.ipynb
+++ b/notebooks/auc_experiments/01-experiment-aggregated.ipynb
@@ -161,13 +161,6 @@
     "print(tmp.to_latex(float_format=\"%.2f\").replace('_', ' '))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": 34,
@@ -222,9 +215,9 @@
     "        p = np.sum(y_test)\n",
     "        n = len(y_test) - np.sum(y_test)\n",
     "\n",
-    "        acc = np.round((tp + tn) / (p + n), 4)\n",
-    "        sens = np.round((tp) / (p), 4)\n",
-    "        spec = np.round((tn) / (n), 4)\n",
+    "        acc = (tp + tn) / (p + n)\n",
+    "        sens = (tp) / (p)\n",
+    "        spec = (tn) / (n)\n",
     "\n",
     "        accs.append(acc)\n",
     "        senss.append(sens)\n",
@@ -242,7 +235,7 @@
     "            tp = np.sum((y_pred >= th) & (y_test == 1))\n",
     "            tn = np.sum((y_pred < th) & (y_test == 0))\n",
     "\n",
-    "            tmp_accs.append(np.round((tp + tn) / len(y_test), 4))\n",
+    "            tmp_accs.append((tp + tn) / len(y_test))\n",
     "\n",
     "        #print(th, np.mean(tmp_accs), best_acc)\n",
     "\n",
@@ -260,9 +253,9 @@
     "        p = np.sum(y_test)\n",
     "        n = len(y_test) - np.sum(y_test)\n",
     "\n",
-    "        best_accs.append(np.round((tp + tn) / len(y_test), 4))\n",
-    "        best_senss.append(np.round((tp) / (p), 4))\n",
-    "        best_specs.append(np.round((tn) / (n), 4))\n",
+    "        best_accs.append((tp + tn) / len(y_test))\n",
+    "        best_senss.append((tp) / (p))\n",
+    "        best_specs.append((tn) / (n))\n",
     "\n",
     "    acc = np.mean(accs)\n",
     "    sens = np.mean(senss)\n",
diff --git a/notebooks/auc_experiments/01-experiment-single.ipynb b/notebooks/auc_experiments/01-experiment-single.ipynb
new file mode 100644
index 0000000..2c71f5a
--- /dev/null
+++ b/notebooks/auc_experiments/01-experiment-single.ipynb
@@ -0,0 +1,298 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import common_datasets.binary_classification as binclas\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "from scipy.stats import wilcoxon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_random_classifier(random_state):\n",
+    "    mode = random_state.randint(4)\n",
+    "    if mode == 0:\n",
+    "        classifier = RandomForestClassifier\n",
+    "        params = {'max_depth': random_state.randint(3, 10),\n",
+    "                  'random_state': 5}\n",
+    "    if mode == 1:\n",
+    "        classifier = DecisionTreeClassifier\n",
+    "        params = {'max_depth': random_state.randint(3, 10),\n",
+    "                  'random_state': 5}\n",
+    "    if mode == 2:\n",
+    "        classifier = SVC\n",
+    "        params = {'probability': True, 'C': random_state.rand()*2 + 0.001}\n",
+    "    if mode == 3:\n",
+    "        classifier = KNeighborsClassifier\n",
+    "        params = {'n_neighbors': random_state.randint(1, 10)}\n",
+    "    \n",
+    "    return (classifier, params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasets = binclas.get_filtered_data_loaders(n_col_bounds=(0, 50), n_bounds=(0, 2000), n_minority_bounds=(20, 1000), n_from_phenotypes=1, imbalance_ratio_bounds=(0.2, 20.0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "28"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(datasets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "names = [dataset()['name'] for dataset in datasets if not dataset()['name'].startswith('led')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from common_datasets.binary_classification import summary_pdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp = summary_pdf[summary_pdf['name'].isin(names)].reset_index(drop=True)\n",
+    "tmp = tmp[['name', 'n_col', 'n', 'n_minority', 'imbalance_ratio', 'citation_key']]\n",
+    "tmp['name_key'] = tmp.apply(lambda row: f'{row[\"name\"]} \\\\cite{{{row[\"citation_key\"]}}}', axis=1)\n",
+    "tmp = tmp[['name_key', 'n', 'n_col', 'n_minority', 'imbalance_ratio']]\n",
+    "tmp.columns = ['name', 'size', 'attr.', 'p', 'imb. ratio']\n",
+    "tmp['n'] = tmp['size'] - tmp['p']\n",
+    "tmp = tmp[['name', 'size', 'attr.', 'p', 'n', 'imb. ratio']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{llrrrrr}\n",
+      "\\toprule\n",
+      " & name & size & attr. & p & n & imb. ratio \\\\\n",
+      "\\midrule\n",
+      "1 & abalone9 18 \\cite{keel} & 731 & 9 & 42 & 689 & 16.40 \\\\\n",
+      "2 & appendicitis \\cite{keel} & 106 & 7 & 21 & 85 & 4.05 \\\\\n",
+      "3 & australian \\cite{keel} & 690 & 16 & 307 & 383 & 1.25 \\\\\n",
+      "4 & bupa \\cite{keel} & 345 & 6 & 145 & 200 & 1.38 \\\\\n",
+      "5 & CM1 \\cite{krnn} & 498 & 21 & 49 & 449 & 9.16 \\\\\n",
+      "6 & crx \\cite{keel} & 653 & 37 & 296 & 357 & 1.21 \\\\\n",
+      "7 & dermatology-6 \\cite{keel} & 358 & 34 & 20 & 338 & 16.90 \\\\\n",
+      "8 & ecoli1 \\cite{keel} & 336 & 7 & 77 & 259 & 3.36 \\\\\n",
+      "9 & glass0 \\cite{keel} & 214 & 9 & 70 & 144 & 2.06 \\\\\n",
+      "10 & haberman \\cite{keel} & 306 & 3 & 81 & 225 & 2.78 \\\\\n",
+      "11 & hepatitis \\cite{krnn} & 155 & 19 & 32 & 123 & 3.84 \\\\\n",
+      "12 & ionosphere \\cite{keel} & 351 & 33 & 126 & 225 & 1.79 \\\\\n",
+      "13 & iris0 \\cite{keel} & 150 & 4 & 50 & 100 & 2.00 \\\\\n",
+      "14 & mammographic \\cite{keel} & 830 & 5 & 403 & 427 & 1.06 \\\\\n",
+      "15 & monk-2 \\cite{keel} & 432 & 6 & 204 & 228 & 1.12 \\\\\n",
+      "16 & new thyroid1 \\cite{keel} & 215 & 5 & 35 & 180 & 5.14 \\\\\n",
+      "17 & page-blocks-1-3 vs 4 \\cite{keel} & 472 & 10 & 28 & 444 & 15.86 \\\\\n",
+      "18 & PC1 \\cite{krnn} & 1109 & 21 & 77 & 1032 & 13.40 \\\\\n",
+      "19 & pima \\cite{keel} & 768 & 8 & 268 & 500 & 1.87 \\\\\n",
+      "20 & saheart \\cite{keel} & 462 & 9 & 160 & 302 & 1.89 \\\\\n",
+      "21 & shuttle-c0-vs-c4 \\cite{keel} & 1829 & 9 & 123 & 1706 & 13.87 \\\\\n",
+      "22 & SPECTF \\cite{krnn} & 267 & 44 & 55 & 212 & 3.85 \\\\\n",
+      "23 & vehicle0 \\cite{keel} & 846 & 18 & 199 & 647 & 3.25 \\\\\n",
+      "24 & vowel0 \\cite{keel} & 988 & 13 & 90 & 898 & 9.98 \\\\\n",
+      "25 & wdbc \\cite{keel} & 569 & 30 & 212 & 357 & 1.68 \\\\\n",
+      "26 & wisconsin \\cite{keel} & 683 & 9 & 239 & 444 & 1.86 \\\\\n",
+      "27 & yeast1 \\cite{keel} & 1484 & 8 & 429 & 1055 & 2.46 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "tmp.index = [idx for idx in range(1, 28)]\n",
+    "print(tmp.to_latex(float_format=\"%.2f\").replace('_', ' '))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[29], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m10000\u001b[39m):\n\u001b[1;32m      5\u001b[0m     loader \u001b[38;5;241m=\u001b[39m random_state\u001b[38;5;241m.\u001b[39mchoice(datasets)\n\u001b[0;32m----> 6\u001b[0m     dataset \u001b[38;5;241m=\u001b[39m loader()\n\u001b[1;32m      7\u001b[0m     X \u001b[38;5;241m=\u001b[39m dataset[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      8\u001b[0m     y \u001b[38;5;241m=\u001b[39m dataset[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtarget\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/common_datasets/binary_classification/_binary_classification_part1.py:866\u001b[0m, in \u001b[0;36mload_spectf\u001b[0;34m()\u001b[0m\n\u001b[1;32m    863\u001b[0m dataset\u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat([db0, db1])\n\u001b[1;32m    864\u001b[0m dataset\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtarget\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mlist\u001b[39m(dataset\u001b[38;5;241m.\u001b[39mcolumns[\u001b[38;5;241m1\u001b[39m:])\n\u001b[0;32m--> 866\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m prepare_csv_data_template(dataset\u001b[38;5;241m=\u001b[39mdataset,\n\u001b[1;32m    867\u001b[0m                     name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSPECTF\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m    868\u001b[0m                     target_label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtarget\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/common_datasets/_io.py:411\u001b[0m, in \u001b[0;36mprepare_csv_data_template\u001b[0;34m(dataset, name, target_label, feature_types, problem_type, citation_key, missing_data)\u001b[0m\n\u001b[1;32m    399\u001b[0m feature_types \u001b[38;5;241m=\u001b[39m coalesce(feature_types, determine_types(dataset))\n\u001b[1;32m    401\u001b[0m dataprep \u001b[38;5;241m=\u001b[39m DataPreprocessor(\n\u001b[1;32m    402\u001b[0m     dataset_raw\u001b[38;5;241m=\u001b[39mdataset,\n\u001b[1;32m    403\u001b[0m     target_label\u001b[38;5;241m=\u001b[39mtarget_label,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    408\u001b[0m     missing_data\u001b[38;5;241m=\u001b[39mmissing_data,\n\u001b[1;32m    409\u001b[0m )\n\u001b[0;32m--> 411\u001b[0m dataset \u001b[38;5;241m=\u001b[39m dataprep\u001b[38;5;241m.\u001b[39mget_dataset()\n\u001b[1;32m    413\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dataset\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/common_datasets/_io.py:1037\u001b[0m, in \u001b[0;36mDataPreprocessor.get_dataset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1035\u001b[0m result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_col\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfeature_names\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m   1036\u001b[0m result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_col_orig\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset_raw\u001b[38;5;241m.\u001b[39mcolumns) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m-> 1037\u001b[0m result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn_col_non_unique_orig\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msum(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset_raw\u001b[38;5;241m.\u001b[39mnunique() \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   1038\u001b[0m result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtarget\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m   1039\u001b[0m result[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDESCR\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdescriptor[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/frame.py:11836\u001b[0m, in \u001b[0;36mDataFrame.nunique\u001b[0;34m(self, axis, dropna)\u001b[0m\n\u001b[1;32m  11798\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnunique\u001b[39m(\u001b[38;5;28mself\u001b[39m, axis: Axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, dropna: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series:\n\u001b[1;32m  11799\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m  11800\u001b[0m \u001b[38;5;124;03m    Count number of distinct elements in specified axis.\u001b[39;00m\n\u001b[1;32m  11801\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m  11834\u001b[0m \u001b[38;5;124;03m    dtype: int64\u001b[39;00m\n\u001b[1;32m  11835\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m> 11836\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply(Series\u001b[38;5;241m.\u001b[39mnunique, axis\u001b[38;5;241m=\u001b[39maxis, dropna\u001b[38;5;241m=\u001b[39mdropna)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m  10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m  10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m  10363\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m  10364\u001b[0m     func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m  10372\u001b[0m     kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m  10373\u001b[0m )\n\u001b[0;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mapply()\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m    914\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_standard()\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1062\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1063\u001b[0m         results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_generator()\n\u001b[1;32m   1064\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1065\u001b[0m         results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_numba()\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/apply.py:1081\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1078\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m   1079\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[1;32m   1080\u001b[0m         \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[0;32m-> 1081\u001b[0m         results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc(v, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs)\n\u001b[1;32m   1082\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[1;32m   1083\u001b[0m             \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[1;32m   1084\u001b[0m             \u001b[38;5;66;03m#  series_generator will swap out the underlying data\u001b[39;00m\n\u001b[1;32m   1085\u001b[0m             results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/base.py:1063\u001b[0m, in \u001b[0;36mIndexOpsMixin.nunique\u001b[0;34m(self, dropna)\u001b[0m\n\u001b[1;32m   1028\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m   1029\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnunique\u001b[39m(\u001b[38;5;28mself\u001b[39m, dropna: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mint\u001b[39m:\n\u001b[1;32m   1030\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1031\u001b[0m \u001b[38;5;124;03m    Return number of unique elements in the object.\u001b[39;00m\n\u001b[1;32m   1032\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1061\u001b[0m \u001b[38;5;124;03m    4\u001b[39;00m\n\u001b[1;32m   1062\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1063\u001b[0m     uniqs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munique()\n\u001b[1;32m   1064\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m dropna:\n\u001b[1;32m   1065\u001b[0m         uniqs \u001b[38;5;241m=\u001b[39m remove_na_arraylike(uniqs)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/series.py:2407\u001b[0m, in \u001b[0;36mSeries.unique\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2344\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munique\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ArrayLike:  \u001b[38;5;66;03m# pylint: disable=useless-parent-delegation\u001b[39;00m\n\u001b[1;32m   2345\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   2346\u001b[0m \u001b[38;5;124;03m    Return unique values of Series object.\u001b[39;00m\n\u001b[1;32m   2347\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2405\u001b[0m \u001b[38;5;124;03m    Categories (3, object): ['a' < 'b' < 'c']\u001b[39;00m\n\u001b[1;32m   2406\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2407\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39munique()\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/base.py:1025\u001b[0m, in \u001b[0;36mIndexOpsMixin.unique\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1023\u001b[0m     result \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39munique()\n\u001b[1;32m   1024\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1025\u001b[0m     result \u001b[38;5;241m=\u001b[39m algorithms\u001b[38;5;241m.\u001b[39munique1d(values)\n\u001b[1;32m   1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/algorithms.py:401\u001b[0m, in \u001b[0;36munique\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m    307\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munique\u001b[39m(values):\n\u001b[1;32m    308\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    309\u001b[0m \u001b[38;5;124;03m    Return unique values based on a hash table.\u001b[39;00m\n\u001b[1;32m    310\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    399\u001b[0m \u001b[38;5;124;03m    array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)\u001b[39;00m\n\u001b[1;32m    400\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 401\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m unique_with_mask(values)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/algorithms.py:429\u001b[0m, in \u001b[0;36munique_with_mask\u001b[0;34m(values, mask)\u001b[0m\n\u001b[1;32m    427\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munique_with_mask\u001b[39m(values, mask: npt\u001b[38;5;241m.\u001b[39mNDArray[np\u001b[38;5;241m.\u001b[39mbool_] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    428\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"See algorithms.unique for docs. Takes a mask for masked arrays.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 429\u001b[0m     values \u001b[38;5;241m=\u001b[39m _ensure_arraylike(values, func_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munique\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    431\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype, ExtensionDtype):\n\u001b[1;32m    432\u001b[0m         \u001b[38;5;66;03m# Dispatch to extension dtype's unique.\u001b[39;00m\n\u001b[1;32m    433\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m values\u001b[38;5;241m.\u001b[39munique()\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/algorithms.py:221\u001b[0m, in \u001b[0;36m_ensure_arraylike\u001b[0;34m(values, func_name)\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_ensure_arraylike\u001b[39m(values, func_name: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ArrayLike:\n\u001b[1;32m    218\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    219\u001b[0m \u001b[38;5;124;03m    ensure that we are arraylike if not already\u001b[39;00m\n\u001b[1;32m    220\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 221\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values, (ABCIndex, ABCSeries, ABCExtensionArray, np\u001b[38;5;241m.\u001b[39mndarray)):\n\u001b[1;32m    222\u001b[0m         \u001b[38;5;66;03m# GH#52986\u001b[39;00m\n\u001b[1;32m    223\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m func_name \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124misin-targets\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    224\u001b[0m             \u001b[38;5;66;03m# Make an exception for the comps argument in isin.\u001b[39;00m\n\u001b[1;32m    225\u001b[0m             warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    226\u001b[0m                 \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m with argument that is not not a Series, Index, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    227\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExtensionArray, or np.ndarray is deprecated and will raise in a \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    230\u001b[0m                 stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    231\u001b[0m             )\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/dtypes/generic.py:44\u001b[0m, in \u001b[0;36mcreate_pandas_abc_type.<locals>._instancecheck\u001b[0;34m(cls, inst)\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m     43\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_instancecheck\u001b[39m(\u001b[38;5;28mcls\u001b[39m, inst) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n\u001b[0;32m---> 44\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _check(inst) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(inst, \u001b[38;5;28mtype\u001b[39m)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/core/dtypes/generic.py:38\u001b[0m, in \u001b[0;36mcreate_pandas_abc_type.<locals>._check\u001b[0;34m(inst)\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_check\u001b[39m(inst) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n\u001b[0;32m---> 38\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(inst, attr, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_typ\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01min\u001b[39;00m comp\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "results = []\n",
+    "random_state = np.random.RandomState(5)\n",
+    "\n",
+    "for _ in range(10000):\n",
+    "    loader = random_state.choice(datasets)\n",
+    "    dataset = loader()\n",
+    "\n",
+    "    X = dataset['data']\n",
+    "    y = dataset['target']\n",
+    "    name = dataset['name']\n",
+    "\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=5)\n",
+    "    classifier = generate_random_classifier(random_state)\n",
+    "\n",
+    "    classifier_obj = classifier[0](**classifier[1])\n",
+    "\n",
+    "    classifier_obj.fit(X_train, y_train)\n",
+    "    y_pred = classifier_obj.predict_proba(X_test)[:, 1]\n",
+    "\n",
+    "    auc = roc_auc_score(y_test, y_pred)\n",
+    "\n",
+    "    threshold = random_state.random()\n",
+    "\n",
+    "    tp = np.sum((y_pred >= threshold) & (y_test == 1))\n",
+    "    tn = np.sum((y_pred < threshold) & (y_test == 0))\n",
+    "    p = np.sum(y_test)\n",
+    "    n = len(y_test) - np.sum(y_test)\n",
+    "\n",
+    "    acc = (tp + tn) / (p + n)\n",
+    "    sens = tp / p\n",
+    "    spec = tn / n\n",
+    "\n",
+    "    best_th = -1\n",
+    "    best_acc = 0\n",
+    "    for th in np.hstack([np.unique(y_pred), np.array([-np.inf, np.inf])]):\n",
+    "        tp = np.sum((y_pred >= th) & (y_test == 1))\n",
+    "        tn = np.sum((y_pred < th) & (y_test == 0))\n",
+    "        p = np.sum(y_test)\n",
+    "        n = len(y_test) - np.sum(y_test)\n",
+    "\n",
+    "        acc_tmp = (tp + tn) / (p + n)\n",
+    "\n",
+    "        if acc_tmp > best_acc:\n",
+    "            best_acc = acc_tmp\n",
+    "            best_th = th\n",
+    "\n",
+    "    th = best_th\n",
+    "\n",
+    "    tp = np.sum((y_pred >= th) & (y_test == 1))\n",
+    "    tn = np.sum((y_pred < th) & (y_test == 0))\n",
+    "    p = np.sum(y_test)\n",
+    "    n = len(y_test) - np.sum(y_test)\n",
+    "\n",
+    "    best_acc = (tp + tn) / (p + n)\n",
+    "    best_sens = (tp) / (p)\n",
+    "    best_spec = (tn) / (n)\n",
+    "\n",
+    "    results.append((name, acc, sens, spec, auc, best_acc, best_sens, best_spec, threshold, best_th, p, n))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.DataFrame(results, columns=['dataset', 'acc', 'sens', 'spec', 'auc', 'best_acc', 'best_sens', 'best_spec', 'threshold', 'best_threshold', 'p', 'n'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.to_csv('raw-single.csv', index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mlscorecheck",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/auc_experiments/03-auc-test.ipynb b/notebooks/auc_experiments/03-auc-test.ipynb
deleted file mode 100644
index 9f6525c..0000000
--- a/notebooks/auc_experiments/03-auc-test.ipynb
+++ /dev/null
@@ -1,271 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import common_datasets.binary_classification as binclas\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "from sklearn.svm import SVC\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import roc_auc_score\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "from scipy.stats import wilcoxon"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def generate_random_classifier(random_state):\n",
-    "    mode = random_state.randint(4)\n",
-    "    if mode == 0:\n",
-    "        classifier = RandomForestClassifier\n",
-    "        params = {'max_depth': random_state.randint(3, 10),\n",
-    "                  'random_state': 5}\n",
-    "    if mode == 1:\n",
-    "        classifier = DecisionTreeClassifier\n",
-    "        params = {'max_depth': random_state.randint(3, 10),\n",
-    "                  'random_state': 5}\n",
-    "    if mode == 2:\n",
-    "        classifier = SVC\n",
-    "        params = {'probability': True, 'C': random_state.rand()*2 + 0.001}\n",
-    "    if mode == 3:\n",
-    "        classifier = KNeighborsClassifier\n",
-    "        params = {'n_neighbors': random_state.randint(1, 10)}\n",
-    "    \n",
-    "    return (classifier, params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "datasets = binclas.get_filtered_data_loaders(n_col_bounds=(0, 50), n_bounds=(0, 2000), n_minority_bounds=(20, 1000), n_from_phenotypes=1, imbalance_ratio_bounds=(0.2, 20.0))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "28"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(datasets)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "names = [dataset()['name'] for dataset in datasets if not dataset()['name'].startswith('led')]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from common_datasets.binary_classification import summary_pdf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tmp = summary_pdf[summary_pdf['name'].isin(names)].reset_index(drop=True)\n",
-    "tmp = tmp[['name', 'n_col', 'n', 'n_minority', 'imbalance_ratio', 'citation_key']]\n",
-    "tmp['name_key'] = tmp.apply(lambda row: f'{row[\"name\"]} \\\\cite{{{row[\"citation_key\"]}}}', axis=1)\n",
-    "tmp = tmp[['name_key', 'n', 'n_col', 'n_minority', 'imbalance_ratio']]\n",
-    "tmp.columns = ['name', 'size', 'attr.', 'p', 'imb. ratio']\n",
-    "tmp['n'] = tmp['size'] - tmp['p']\n",
-    "tmp = tmp[['name', 'size', 'attr.', 'p', 'n', 'imb. ratio']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\\begin{tabular}{llrrrrr}\n",
-      "\\toprule\n",
-      " & name & size & attr. & p & n & imb. ratio \\\\\n",
-      "\\midrule\n",
-      "1 & abalone9 18 \\cite{keel} & 731 & 9 & 42 & 689 & 16.40 \\\\\n",
-      "2 & appendicitis \\cite{keel} & 106 & 7 & 21 & 85 & 4.05 \\\\\n",
-      "3 & australian \\cite{keel} & 690 & 16 & 307 & 383 & 1.25 \\\\\n",
-      "4 & bupa \\cite{keel} & 345 & 6 & 145 & 200 & 1.38 \\\\\n",
-      "5 & CM1 \\cite{krnn} & 498 & 21 & 49 & 449 & 9.16 \\\\\n",
-      "6 & crx \\cite{keel} & 653 & 37 & 296 & 357 & 1.21 \\\\\n",
-      "7 & dermatology-6 \\cite{keel} & 358 & 34 & 20 & 338 & 16.90 \\\\\n",
-      "8 & ecoli1 \\cite{keel} & 336 & 7 & 77 & 259 & 3.36 \\\\\n",
-      "9 & glass0 \\cite{keel} & 214 & 9 & 70 & 144 & 2.06 \\\\\n",
-      "10 & haberman \\cite{keel} & 306 & 3 & 81 & 225 & 2.78 \\\\\n",
-      "11 & hepatitis \\cite{krnn} & 155 & 19 & 32 & 123 & 3.84 \\\\\n",
-      "12 & ionosphere \\cite{keel} & 351 & 33 & 126 & 225 & 1.79 \\\\\n",
-      "13 & iris0 \\cite{keel} & 150 & 4 & 50 & 100 & 2.00 \\\\\n",
-      "14 & mammographic \\cite{keel} & 830 & 5 & 403 & 427 & 1.06 \\\\\n",
-      "15 & monk-2 \\cite{keel} & 432 & 6 & 204 & 228 & 1.12 \\\\\n",
-      "16 & new thyroid1 \\cite{keel} & 215 & 5 & 35 & 180 & 5.14 \\\\\n",
-      "17 & page-blocks-1-3 vs 4 \\cite{keel} & 472 & 10 & 28 & 444 & 15.86 \\\\\n",
-      "18 & PC1 \\cite{krnn} & 1109 & 21 & 77 & 1032 & 13.40 \\\\\n",
-      "19 & pima \\cite{keel} & 768 & 8 & 268 & 500 & 1.87 \\\\\n",
-      "20 & saheart \\cite{keel} & 462 & 9 & 160 & 302 & 1.89 \\\\\n",
-      "21 & shuttle-c0-vs-c4 \\cite{keel} & 1829 & 9 & 123 & 1706 & 13.87 \\\\\n",
-      "22 & SPECTF \\cite{krnn} & 267 & 44 & 55 & 212 & 3.85 \\\\\n",
-      "23 & vehicle0 \\cite{keel} & 846 & 18 & 199 & 647 & 3.25 \\\\\n",
-      "24 & vowel0 \\cite{keel} & 988 & 13 & 90 & 898 & 9.98 \\\\\n",
-      "25 & wdbc \\cite{keel} & 569 & 30 & 212 & 357 & 1.68 \\\\\n",
-      "26 & wisconsin \\cite{keel} & 683 & 9 & 239 & 444 & 1.86 \\\\\n",
-      "27 & yeast1 \\cite{keel} & 1484 & 8 & 429 & 1055 & 2.46 \\\\\n",
-      "\\bottomrule\n",
-      "\\end{tabular}\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "tmp.index = [idx for idx in range(1, 28)]\n",
-    "print(tmp.to_latex(float_format=\"%.2f\").replace('_', ' '))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = []\n",
-    "random_state = np.random.RandomState(5)\n",
-    "\n",
-    "for _ in range(10000):\n",
-    "    loader = random_state.choice(datasets)\n",
-    "    dataset = loader()\n",
-    "    X = dataset['data']\n",
-    "    y = dataset['target']\n",
-    "    name = dataset['name']\n",
-    "\n",
-    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=5)\n",
-    "    classifier = generate_random_classifier(random_state)\n",
-    "\n",
-    "    classifier_obj = classifier[0](**classifier[1])\n",
-    "\n",
-    "    classifier_obj.fit(X_train, y_train)\n",
-    "    y_pred = classifier_obj.predict_proba(X_test)[:, 1]\n",
-    "\n",
-    "    auc = roc_auc_score(y_test, y_pred)\n",
-    "\n",
-    "    threshold = random_state.random()\n",
-    "\n",
-    "    tp = np.sum((y_pred >= threshold) & (y_test == 1))\n",
-    "    tn = np.sum((y_pred < threshold) & (y_test == 0))\n",
-    "    p = np.sum(y_test)\n",
-    "    n = len(y_test) - np.sum(y_test)\n",
-    "\n",
-    "    acc = np.round((tp + tn) / (p + n), 4)\n",
-    "    sens = np.round((tp) / (p), 4)\n",
-    "    spec = np.round((tn) / (n), 4)\n",
-    "\n",
-    "    best_th = -1\n",
-    "    best_acc = 0\n",
-    "    for th in np.unique(y_pred):\n",
-    "        tp = np.sum((y_pred >= th) & (y_test == 1))\n",
-    "        tn = np.sum((y_pred < th) & (y_test == 0))\n",
-    "        p = np.sum(y_test)\n",
-    "        n = len(y_test) - np.sum(y_test)\n",
-    "\n",
-    "        acc = np.round((tp + tn) / (p + n), 4)\n",
-    "        sens = np.round((tp) / (p), 4)\n",
-    "        spec = np.round((tn) / (n), 4)\n",
-    "\n",
-    "        if acc > best_acc:\n",
-    "            best_acc = acc\n",
-    "            best_th = th\n",
-    "\n",
-    "    th = best_th\n",
-    "\n",
-    "    tp = np.sum((y_pred >= th) & (y_test == 1))\n",
-    "    tn = np.sum((y_pred < th) & (y_test == 0))\n",
-    "    p = np.sum(y_test)\n",
-    "    n = len(y_test) - np.sum(y_test)\n",
-    "\n",
-    "    best_acc = np.round((tp + tn) / (p + n), 4)\n",
-    "    best_sens = np.round((tp) / (p), 4)\n",
-    "    best_spec = np.round((tn) / (n), 4)\n",
-    "\n",
-    "    results.append((name, acc, sens, spec, auc, best_acc, best_sens, best_spec, threshold, best_th, p, n))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.DataFrame(results, columns=['dataset', 'acc', 'sens', 'spec', 'auc', 'best_acc', 'best_sens', 'best_spec', 'threshold', 'best_threshold', 'p', 'n'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data.to_csv('raw-single.csv', index=False)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "mlscorecheck",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/auc_experiments/05-results-intervals-table.ipynb b/notebooks/auc_experiments/05-results-intervals-table.ipynb
new file mode 100644
index 0000000..3c301ee
--- /dev/null
+++ b/notebooks/auc_experiments/05-results-intervals-table.ipynb
@@ -0,0 +1,99 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'results-intervals-single.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m a \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresults-intervals-single.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      2\u001b[0m b \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresults-intervals-aggregated.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      3\u001b[0m c \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresults-intervals-aggregated-ns.csv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m   1014\u001b[0m     dialect,\n\u001b[1;32m   1015\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[1;32m   1023\u001b[0m )\n\u001b[1;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/io/parsers/readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[1;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_engine(f, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine)\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1878\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1879\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m get_handle(\n\u001b[1;32m   1881\u001b[0m     f,\n\u001b[1;32m   1882\u001b[0m     mode,\n\u001b[1;32m   1883\u001b[0m     encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m   1884\u001b[0m     compression\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompression\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m   1885\u001b[0m     memory_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmemory_map\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[1;32m   1886\u001b[0m     is_text\u001b[38;5;241m=\u001b[39mis_text,\n\u001b[1;32m   1887\u001b[0m     errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mencoding_errors\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m   1888\u001b[0m     storage_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstorage_options\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[1;32m   1889\u001b[0m )\n\u001b[1;32m   1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
+      "File \u001b[0;32m~/anaconda3/envs/mlscorecheck/lib/python3.12/site-packages/pandas/io/common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    869\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    870\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    871\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    872\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 873\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\n\u001b[1;32m    874\u001b[0m             handle,\n\u001b[1;32m    875\u001b[0m             ioargs\u001b[38;5;241m.\u001b[39mmode,\n\u001b[1;32m    876\u001b[0m             encoding\u001b[38;5;241m=\u001b[39mioargs\u001b[38;5;241m.\u001b[39mencoding,\n\u001b[1;32m    877\u001b[0m             errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[1;32m    878\u001b[0m             newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    879\u001b[0m         )\n\u001b[1;32m    880\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    881\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    882\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'results-intervals-single.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "a = pd.read_csv('results-intervals-single.csv')\n",
+    "b = pd.read_csv('results-intervals-aggregated.csv')\n",
+    "c = pd.read_csv('results-intervals-aggregated-ns.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.concat([a, b[b.columns[3:]], c[c.columns[3:]]], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.columns = pd.MultiIndex.from_tuples([\n",
+    "    ('', 'target'),\n",
+    "    ('', 'source'),\n",
+    "    ('', 'estimation'),\n",
+    "    ('single test set', 'avg. lower'),\n",
+    "    ('single test set', 'avg. upper'),\n",
+    "    ('k-fold', 'avg. lower'),\n",
+    "    ('k-fold', 'avg. upper'),\n",
+    "    ('k-fold no strat.', 'avg. lower'),\n",
+    "    ('k-fold no strat.', 'avg. upper')]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(data.to_latex(index=False, float_format=\"%.3f\"))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mlscorecheck",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/auc_experiments/05-results-intervals.ipynb b/notebooks/auc_experiments/05-results-intervals.ipynb
index 25c9fa9..f58025a 100644
--- a/notebooks/auc_experiments/05-results-intervals.ipynb
+++ b/notebooks/auc_experiments/05-results-intervals.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 468,
+   "execution_count": 343,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15,15 +15,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 469,
+   "execution_count": 344,
    "metadata": {},
    "outputs": [],
    "source": [
-    "label = 'aggregated-ns'\n",
-    "clabel = 'avg.'\n",
+    "#label = 'aggregated-ns'\n",
+    "#clabel = 'avg.'\n",
     "\n",
-    "label = 'aggregated'\n",
-    "clabel = 'avg.'\n",
+    "#label = 'aggregated'\n",
+    "#clabel = 'avg.'\n",
     "\n",
     "label = 'single'\n",
     "clabel = ''"
@@ -31,7 +31,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 470,
+   "execution_count": 345,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 346,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,23 +49,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 471,
+   "execution_count": 347,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Index(['Unnamed: 0', 'dataset', 'k', 'acc', 'sens', 'spec', 'auc', 'best_acc',\n",
-       "       'best_sens', 'best_spec', 'threshold', 'best_threshold',\n",
-       "       'best_acc_orig', 'p', 'n', 'auc_min', 'auc_min_best', 'auc_rmin',\n",
-       "       'auc_rmin_best', 'auc_amin', 'auc_amin_best', 'auc_armin',\n",
+       "Index(['dataset', 'acc', 'sens', 'spec', 'auc', 'best_acc', 'best_sens',\n",
+       "       'best_spec', 'threshold', 'best_threshold', 'p', 'n', 'auc_min',\n",
+       "       'auc_min_best', 'auc_rmin', 'auc_rmin_best', 'auc_grmin',\n",
+       "       'auc_grmin_best', 'auc_amin', 'auc_amin_best', 'auc_armin',\n",
        "       'auc_armin_best', 'auc_max', 'auc_max_best', 'auc_amax',\n",
        "       'auc_amax_best', 'auc_maxa', 'auc_maxa_best', 'acc_min', 'acc_rmin',\n",
        "       'acc_max', 'acc_rmax', 'max_acc_min', 'max_acc_max', 'max_acc_rmax'],\n",
        "      dtype='object')"
       ]
      },
-     "execution_count": 471,
+     "execution_count": 347,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -67,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 472,
+   "execution_count": 348,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 473,
+   "execution_count": 349,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 475,
+   "execution_count": 350,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,12 +118,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 476,
+   "execution_count": 351,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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wXIwx1hxKJ9ODBw/KLRMR8vPzsWHDBuGtBUVoa2vD2dkZycnJwjXT2tpaJCcnY8aMGY2ut2LFCvzrX/9CUlISXFxclA2fMcZUQulk+uzNIuDpw7Dm5uZ49913sXr1aqXaCgkJQWBgIFxcXDBo0CBERUWhvLxcuLs/ceJEdOnSBZGRkQCA5cuXIzw8HDt37oSdnZ1wbdXQ0JCH/mOMqVWz3s0Xi7+/P+7du4fw8HAUFBTAyckJR48eFW5K5eXlQUPjf5d1v/32W1RXV+Nvf/ubXDsRERH48ssvRYuLsbaG52RrGTGOn9ofL54xY0ajp/UpKSlyyzdv3lR9QKzNaO3xOl9Gda9NVlRUyA2JyZRTN6RfSwaZVjqZhoSEKFx3zZo1yjbPWJuQVrK70e82aVwT/j3dpOkbpC2lqakJExMTYRAPfX19SCQSlW7zVUJEqKioQFFREUxMTKCpqdnstpROpunp6UhPT0dNTY0wnFVWVhY0NTUxcOBAoR7/QBlrHXVPv6h6VKRXmYmJyQufInoRpZPp6NGjIZVKER8fL7wN9fDhQwQFBWHIkCGYO3duiwJijClHIpHA2toaFhYWqKmpUXc4bU6HDh1a1COt06wh+I4dOyb3WmnHjh2xdOlSjBw5kpMpY2qiqakpSlJgzaP0G1AlJSW4d+9evfJ79+6htLRUlKAYY6ytUTqZfvjhhwgKCsLevXtx584d3LlzB3v27EFwcDD8/PxUESNjjL30lD7Nj46Oxrx58zBu3Djh+oyWlhaCg4OxcuVK0QNkjLG2QOlkqq+vj02bNmHlypXIyckBAPTo0aPB6QcYY6y9aPZD+/n5+cjPz8fQoUOhp6cHIuLHodqZtccbH22+KXNG9BI5EsbUT+lrpsXFxRg+fDh69eqFUaNGCdOzBgcH8518xli7pXQynTNnDjp06IC8vDzo6+sL5f7+/jh69KiowTHGWFuh9Gn+sWPHkJSUhK5du8qV29vb49atW6IFxhh7xslI8dts4ZQpTJ7SybS8vFyuR1rnwYMHPAgzUwhfa32GKpIkUwulT/OHDBkiN0OpRCJBbW0tVqxYgXfeeUfU4BhjrK1Qume6YsUKDB8+HBcuXEB1dTU+//xzXL16FQ8ePMDp06dVESNjLdLULKOMiUXpnmnfvn2RlZWFwYMHw8fHB+Xl5fDz80N6ejp69OihihgZY+ylp1TPtKamBt7e3oiOjsY///lPVcXEGGNtjlI90w4dOiAjI0NVsTDGWJul9Gn++PHjsXXrVlXEwhhjbZbSN6CePHmC2NhYnDhxAs7OzvXeyeepShhj7ZFCyTQjIwN9+/aFhoYGfv31V2F6kqws+ecF+d18xlh7pVAyHTBgAPLz82FhYYFbt24hNTUVpqamqo6NMcbaDIWumZqYmCA3NxfA0+mWa2trVRoUY4y1NQr1TP/617/Cw8MD1tbWkEgkcHFxaXSumd9//13UABljrC1QKJnGxMTAz88P2dnZ+OyzzzBlyhRIpVJVx8baoabmo5+wpxUDYUxJCt/N9/b2BgBcvHgRs2bN4mTKWFunzCArPMLUCyn9aNS2bdtUEQdTo+aO4sQY+x+lH9pnjDFWHydTxhgTASdTxhgTASdTxhgTASdTxhgTASdTxhgTgdKPRjHV4wnnGGt7uGfKGGMi4J7pK4QfvmdMfbhnyhhjIuBkyhhjIuBkyhhjIuBrpoyxF1N0hKl2PLoU90wZY0wE3DNlraKpQZ8ZexVwz5QxxkTAyZQxxkTAp/kqxA/RM9Z+cM+UMcZEwD1T1mx8U4mx/+GeKWOMiYB7powx8bTjh/vV3jPduHEj7OzsoKurC1dXV5w/f77J+omJiXj99dehq6sLR0dHHD58uJUiZYyxxqm1Z7p7926EhIQgOjoarq6uiIqKgpeXFzIzM2FhYVGv/pkzZzB27FhERkbigw8+wM6dO+Hr64u0tDT07dtXZXHyXXnG2ItIiIjUtXFXV1e8+eab2LBhAwCgtrYWNjY2mDlzJr744ot69f39/VFeXo4ff/xRKHvrrbfg5OSE6OhohbZZUlICY2NjPH78GEZGRgqtw8m0YXwDqmFuGteEf0836afGSF4Rar4koGjOUFvPtLq6GhcvXkRY2P8OlIaGBjw9PXH27NkG1zl79ixCQkLkyry8vLB//35VhspYs216lCF6m+0uQbeR67BqS6b379+HTCaDpaWlXLmlpSVu3LjR4DoFBQUN1i8oKGh0O1VVVaiqqhKWHz9+DODpXxtFVZaXKVy3PamuqFR3CC+lPzVqVNr+6oqLCtedYvyGCiN5yfwYoXjdoXMVrlqXK150Ev/K382PjIzE4sWL65Xb2NioIRrWHvyg7gCeMQ971B3CS+orpdcoLS2FsbFxo9+rLZmamZlBU1MThYWFcuWFhYWwsrJqcB0rKyul6gNAWFiY3KWB2tpaPHjwAKamppBIJC+Ms6SkBDY2Nrh9+7bC11jbAz4uDePj0ri2emyICKWlpejcuXOT9dSWTLW1teHs7Izk5GT4+voCeJrokpOTMWPGjAbXcXNzQ3JyMmbPni2UHT9+HG5ubo1uR0dHBzo6OnJlJiYmSsdrZGTUpn4BWgsfl4bxcWlcWzw2TfVI66j1ND8kJASBgYFwcXHBoEGDEBUVhfLycgQFBQEAJk6ciC5duiAy8ukF6FmzZsHDwwOrV6/G+++/j4SEBFy4cAExMTHq3A3GGFNvMvX398e9e/cQHh6OgoICODk54ejRo8JNpry8PGho/O+9And3d+zcuRMLFy7EggULYG9vj/3796v0GVPGGFMIsSZVVlZSREQEVVZWqjuUlwofl4bxcWncq35s1PrQPmOMvSrU/m4+Y4y9CjiZMsaYCDiZMsaYCDiZNuBf//oX3N3doa+vr/AzqUSE8PBwWFtbQ09PD56envjtt99UG2gre/DgAQICAmBkZAQTExMEBwejrKzpV22HDRsGiUQi9/nHP/7RShGrBg8b2TBljktcXFy93wtdXd1WjFZ8nEwbUF1djY8++gjTpk1TeJ0VK1bgm2++QXR0NM6dOwcDAwN4eXmhsvLVeX89ICAAV69exfHjx/Hjjz/i559/xieffPLC9aZMmYL8/Hzhs2LFilaIVjXqho2MiIhAWloa+vfvDy8vLxQVFTVYv27YyODgYKSnp8PX1xe+vr749ddfWzly1VL2uABPH95/9vfi1q1brRixCqj5aYKX2rZt28jY2PiF9Wpra8nKyopWrlwplD169Ih0dHRo165dKoyw9Vy7do0AUGpqqlB25MgRkkgk9McffzS6noeHB82aNasVImwdgwYNok8//VRYlslk1LlzZ4qMjGyw/scff0zvv/++XJmrqytNnTpVpXG2NmWPi6L/t9oS7pmKIDc3FwUFBfD09BTKjI2N4erq2uhwgm3N2bNnYWJiAhcXF6HM09MTGhoaOHfuXJPr7tixA2ZmZujbty/CwsJQUVGh6nBVom7YyGd/zooMG/lsfeDpsJGvyu8F0LzjAgBlZWWwtbWFjY0NfHx8cPXq1dYIV2Ve+VGjWkPdEIDKDg/YlhQUFNSb/UBLSwudOnVqch/HjRsHW1tbdO7cGRkZGQgNDUVmZib27t2r6pBF11rDRrY1zTkuDg4OiI2NRb9+/fD48WOsWrUK7u7uuHr1Krp27doaYYuu3fRMv/jii3oXvJ//NPaDf5Wp+rh88skn8PLygqOjIwICArB9+3bs27cPOTk5Iu4Fa2vc3NwwceJEODk5wcPDA3v37oW5uTk2b96s7tCard30TOfOnYtJkyY1Wee1115rVtt1QwAWFhbC2tpaKC8sLISTk1Oz2mwtih4XKyurejcTnjx5ggcPHjQ5BOLzXF1dAQDZ2dno0aOH0vGqU2sNG9nWNOe4PK9Dhw4YMGAAsrOzVRFiq2g3ydTc3Bzm5uYqabt79+6wsrJCcnKykDxLSkpw7tw5pZ4IUAdFj4ubmxsePXqEixcvwtnZGQDw008/oba2VkiQirh06RIAyP3RaStaa9jItqY5x+V5MpkMV65cwahRo1QYqYqp+w7Yy+jWrVuUnp5OixcvJkNDQ0pPT6f09HQqLS0V6jg4ONDevXuF5WXLlpGJiQkdOHCAMjIyyMfHh7p3705//vmnOnZBJby9vWnAgAF07tw5+uWXX8je3p7Gjh0rfH/nzh1ycHCgc+fOERFRdnY2ffXVV3ThwgXKzc2lAwcO0GuvvUZDhw5V1y60WEJCAuno6FBcXBxdu3aNPvnkEzIxMaGCggIiIpowYQJ98cUXQv3Tp0+TlpYWrVq1iq5fv04RERHUoUMHunLlirp2QSWUPS6LFy+mpKQkysnJoYsXL9KYMWNIV1eXrl69qq5daDFOpg0IDAwkAPU+J0+eFOoAoG3btgnLtbW1tGjRIrK0tCQdHR0aPnw4ZWZmtn7wKlRcXExjx44lQ0NDMjIyoqCgILk/MLm5uXLHKS8vj4YOHUqdOnUiHR0d6tmzJ82fP58eP36spj0Qx/r166lbt26kra1NgwYNov/+97/Cdx4eHhQYGChX/4cffqBevXqRtrY2vfHGG3To0KFWjrh1KHNcZs+eLdS1tLSkUaNGUVpamhqiFg+PGsUYYyJoN3fzGWNMlTiZMsaYCDiZMsaYCDiZMsaYCDiZMsaYCDiZMsaYCDiZMsaYCDiZMsaYCDiZqsCwYcPk3sW2s7NDVFSUsFxQUIARI0bAwMBAmBalobJX0aJFixQanb8lJk2aJLwj3tpu3rwJiUQijEHAWs9bb72FPXv2qG377WagE3VKTU2FgYGBsLx27Vrk5+fj0qVLMDY2brTsVVNQUIB169bhypUrorR38+ZNdO/eHenp6XKjc61btw6t8WLfpEmT8OjRI+zfv18os7GxQX5+PszMzFS+fSZv4cKFmDNnDj788ENoaLR+P5F7pq3A3Nwc+vr6wnJOTg6cnZ1hb28vDLjcUJmyqqurRYlXVf7973/D3d0dtra2Kt2OsbGx2nr3mpqasLKygpYW91Na23vvvYfS0lIcOXJEPQGoeWyANq+srIwmTJhABgYGZGVlRatWrao375GtrS2tXbtW+DeeGTwlMDCwwTIioocPH1JwcDCZmZmRVCqld955hy5duiS0GxERQf3796ctW7aQnZ0dSSQSpdbbvn072drakpGREfn7+1NJSYlQRyaT0fLly6lHjx6kra1NNjY2tHTpUuH7vLw8+uijj8jY2Jg6duxIf/nLXyg3N7fJY/XGG2/Qhg0b5MpkMhl9/fXXZGdnR7q6utSvXz9KTEwUvn/w4AGNGzeOzMzMSFdXl3r27EmxsbFERPUGovHw8CCipwPV+Pj4CG14eHjQjBkzaNasWWRiYkIWFhYUExNDZWVlNGnSJDI0NKQePXrQ4cOHhXWePHlCkydPFuLq1asXRUVFyR3D57d/8uRJYbCX9PR0oW5KSgq9+eabpK2tTVZWVhQaGko1NTVy8c2cOZPmz59PHTt2JEtLS4qIiGjyWCri/v37NGbMGOrcuTPp6elR3759aefOncL3mzdvJmtra5LJZHLr/eUvf6GgoCBhecmSJWRubk6GhoYUHBxMoaGh1L9/f6Vi2b59Ozk7O5OhoSFZWlrS2LFjqbCwUPi+oTmh9u3bR8+nqIMHD5KLiwvp6OiQqakp+fr6yn0fFBRE48ePVyo2sXAybaFp06ZRt27d6MSJE5SRkUEffPABSaXSRpNpUVEReXt708cff0z5+fn06NGjBsuIiDw9PWn06NGUmppKWVlZNHfuXDI1NaXi4mIievof2sDAgLy9vSktLY0uX76s8HqGhobk5+dHV65coZ9//pmsrKxowYIFQsyff/45dezYkeLi4ig7O5tOnTpFW7ZsISKi6upq6t27N02ePJkyMjLo2rVrNG7cOHJwcKCqqqoGj1NxcTFJJBK5kYSIiJYuXUqvv/46HT16lHJycmjbtm2ko6NDKSkpRET06aefkpOTE6WmplJubi4dP36cDh48SERE58+fJwB04sQJys/PF/avoWQqlUppyZIllJWVRUuWLCFNTU167733KCYmhrKysmjatGlkampK5eXlwj6Gh4dTamoq/f777/T999+Tvr4+7d69m4iISktL6eOPPyZvb2/Kz8+n/Px8qqqqqpdM79y5Q/r6+jR9+nS6fv067du3j8zMzOSSpYeHBxkZGdGXX35JWVlZFB8fTxKJhI4dO/aC376m3blzh1auXEnp6emUk5ND33zzDWlqagpDJD548IC0tbXpxIkTcj+nZ8u+//570tXVpdjYWMrMzKTFixeTkZGR0sl069atdPjwYcrJyaGzZ8+Sm5sbvffee8L3iiTTH3/8kTQ1NSk8PJyuXbtGly5doq+//lpunW+//ZZsbW2Vik0snExboLS0lLS1temHH34QyoqLi0lPT6/RZEpE5OPjU2+YtufLTp06RUZGRlRZWSlXr0ePHrR582YiImFszKKiIqXX09fXl+uJzp8/n1xdXYmIqKSkhHR0dITk+bzvvvuOHBwcqLa2ViirqqoiPT09SkpKanCd9PR0AkB5eXlCWWVlJenr69OZM2fk6gYHBwvjpI4ePVqul/SshnqBRA0n08GDBwvLT548IQMDA5owYYJQlp+fTwDo7NmzDW6L6Gli/+tf/9rodhqKacGCBfWO1caNG8nQ0FDoET4fHxHRm2++SaGhoY3G0lzvv/8+zZ07V1j28fGhyZMnC8ubN2+mzp07C7G5urrKzTpKRPT2228rnUyfl5qaSgCEIRwVSaZubm4UEBDQZLsHDhwgDQ2Ner3t1sDXTFsgJycH1dXVciPNd+rUCQ4ODi1u+/LlyygrK4OpqSkMDQ2FT25urtz8Sba2tnIj5Su6np2dHaRSqbBsbW0tTEty/fp1VFVVYfjw4Y3Glp2dDalUKrTfqVMnVFZWNjq3059//gkA0NXVFcqys7NRUVGBESNGyMW6fft2oZ1p06YhISEBTk5O+Pzzz3HmzBllDyUAoF+/fsK/NTU1YWpqCkdHR6GsbjK4Z6dm2bhxI5ydnWFubg5DQ0PExMQgLy9Pqe1ev34dbm5ukEgkQtnbb7+NsrIy3Llzp8H4APmfx/NOnTold7x27NjRYD2ZTIYlS5bA0dERnTp1gqGhIZKSkuT2ISAgAHv27EFVVRWApzPJjhkzRriBk5mZiUGDBsm1+/yyIi5evIjRo0ejW7dukEql8PDwAACljuelS5ca/Z2so6enh9raWmF/WhNfJX9JlZWVwdraGikpKfW+e/bmyrNPCSizXocOHeS+k0gkqK2tBfD0F/JFsTk7Ozf4n7ixKVDq7m4/fPhQqFNWVgYAOHToELp06SJXX0dHB8DTmwq3bt3C4cOHcfz4cQwfPhyffvopVq1a1WSMz2tof58tq0t2dccgISEB8+bNw+rVq+Hm5gapVIqVK1e+cFrr5mrq5/E8FxcXuUevnp8VtM7KlSuxbt06REVFwdHREQYGBpg9e7bcjcrRo0eDiHDo0CG8+eabOHXqFNauXdvyHXpGeXk5vLy84OXlhR07dsDc3Bx5eXnw8vISYtHQ0Kj3BEZNTY3c8ot+LwHgwYMHMDAwUKiu2DiZtkCPHj3QoUMHnDt3Dt26dQPwNFlkZWUJf3mba+DAgSgoKICWlhbs7OxUvt6z7O3toaenh+TkZPz9739vcBu7d++GhYUFjIyMFGqzR48eMDIywrVr19CrVy8AQJ8+faCjo4O8vLwmj5e5uTkCAwMRGBiIIUOGYP78+Vi1ahW0tbUBPO2Bie306dNwd3fH9OnThbLne93a2tov3Hbv3r2xZ88eEJGQsE+fPg2pVNrsKY319PTQs2fPF9Y7ffo0fHx8MH78eABP/1BkZWWhT58+Qh1dXV34+flhx44dyM7OhoODAwYOHCh87+DggNTUVEycOFEoS01NVSreGzduoLi4GMuWLYONjQ0A4MKFC3J1zM3NUVpaivLycqGD8Pyzuv369UNycjKCgoIa3davv/6KAQMGKBWfWPg0vwUMDQ0RHByM+fPn46effsKvv/6KSZMmifKMm6enJ9zc3ODr64tjx47h5s2bOHPmDP75z3/W+0UUY71n6erqIjQ0FJ9//rlwyv3f//4XW7duBfD01NDMzAw+Pj44deoUcnNzkZKSgs8++0zu1PVZGhoa8PT0xC+//CKUSaVSzJs3D3PmzEF8fDxycnKQlpaG9evXIz4+HgAQHh6OAwcOIDs7G1evXsWPP/6I3r17AwAsLCygp6eHo0ePorCwEI8fP1Zo/xRhb2+PCxcuICkpCVlZWVi0aFG9JGJnZ4eMjAxkZmbi/v379XpSADB9+nTcvn0bM2fOxI0bN3DgwAFEREQgJCRE5c9C2tvb4/jx4zhz5gyuX7+OqVOn1ptBFHj68zx06BBiY2MREBAg993MmTOxdetWxMfH47fffsPSpUuRkZEhd9li3759eP311xuNo1u3btDW1sb69evx+++/4+DBg1iyZIlcHVdXV+jr62PBggXIycnBzp07ERcXJ1cnIiICu3btQkREBK5fv44rV65g+fLlcnVOnTqFkSNHKnqIRMXJtIVWrlyJIUOGYPTo0fD09MTgwYOF2TtbQiKR4PDhwxg6dCiCgoLQq1cvjBkzBrdu3Wr0tK4l6z1v0aJFmDt3LsLDw9G7d2/4+/sL1/D09fXx888/o1u3bvDz80Pv3r0RHByMysrKJnuqf//735GQkCB3+rpkyRIsWrQIkZGR6N27N7y9vXHo0CF0794dwNPeX1hYGPr164ehQ4dCU1MTCQkJAAAtLS1888032Lx5Mzp37gwfHx+F9+9Fpk6dCj8/P/j7+8PV1RXFxcVyvVQAmDJlChwcHODi4gJzc3OcPn26XjtdunTB4cOHcf78efTv3x//+Mc/EBwcjIULF4oWa2MWLlyIgQMHwsvLC8OGDYOVlVWDb4a9++676NSpEzIzMzFu3Di57wICAhAWFoZ58+Zh4MCByM3NxaRJk+SufT9+/BiZmZmNxmFubo64uDgkJiaiT58+WLZsWb3LNJ06dcL333+Pw4cPw9HREbt27cKXX34pV2fYsGFITEzEwYMH4eTkhHfffRfnz58Xvv/jjz9w5syZJnuuqsRzQLFWQ0RwdXXFnDlzMHbsWHWHw5ppxIgRsLKywnfffafuUOSEhobi4cOHiImJUcv2+ZopazUSiQQxMTGivU7KVK+iogLR0dHw8vKCpqYmdu3ahRMnTuD48ePqDq0eCwsLhISEqG373DNljDXqzz//xOjRo5Geno7Kyko4ODhg4cKF8PPzU3doLx1OpowxJgK+AcUYYyLgZMoYYyLgZMoYYyLgZMoYYyLgZMoYYyLgZMoYYyLgZMoYYyLgZMoYYyLgZMoYYyL4P4vlTAajy6s5AAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 350x200 with 1 Axes>"
       ]
@@ -132,17 +141,17 @@
     "plt.ylabel('frequency')\n",
     "plt.legend()\n",
     "plt.tight_layout()\n",
-    "plt.savefig(f'{label}-auc-diffs-hist.pdf')"
+    "plt.savefig(f'figures-intervals/{label}-auc-diffs-hist.pdf')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 477,
+   "execution_count": 352,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 350x350 with 1 Axes>"
       ]
@@ -167,17 +176,32 @@
     "plt.ylabel(r'(rmin, max) width')\n",
     "plt.legend()\n",
     "plt.tight_layout()\n",
-    "plt.savefig(f'{label}-auc-interval-scatter.pdf')"
+    "plt.savefig(f'figures-intervals/{label}-auc-interval-scatter.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 353,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.append({'target': ['auc', 'auc'],\n",
+    "                'source': ['arbitrary fpr, tpr', 'arbitrary fpr, tpr'],\n",
+    "                'estimation': ['(min, max)', '(rmin, max)'],\n",
+    "                'avg. lower': [np.mean(data['auc_min'] - data['auc']),\n",
+    "                               np.mean(data['auc_rmin'] - data['auc'])],\n",
+    "                'avg. upper': [np.mean(data['auc_max'] - data['auc']),\n",
+    "                              np.mean(data['auc_max'] - data['auc'])]})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 478,
+   "execution_count": 354,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 350x200 with 1 Axes>"
       ]
@@ -196,17 +220,17 @@
     "plt.ylabel('frequency')\n",
     "plt.legend()\n",
     "plt.tight_layout()\n",
-    "plt.savefig(f'{label}-auc-macc-diffs-hist.pdf')"
+    "plt.savefig(f'figures-intervals/{label}-auc-macc-diffs-hist.pdf')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 479,
+   "execution_count": 355,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 350x350 with 1 Axes>"
       ]
@@ -226,58 +250,50 @@
     "            s=5,\n",
     "            label='(min, max) vs. (min, maxa)'\n",
     "            )\n",
-    "plt.scatter(data['auc_max_best'] - data['auc_rmin_best'], \n",
+    "plt.scatter(data['auc_max_best'] - data['auc_min_best'], \n",
     "            data['auc_maxa_best'] - data['auc_rmin_best'], \n",
     "            alpha=0.5, \n",
     "            s=5,\n",
-    "            label='(rmin, max) vs. (rmin, maxa)'\n",
+    "            label='(min, max) vs. (rmin, maxa)'\n",
     "            )\n",
     "plt.plot([0, min(valx, valy)], [0, min(valx, valy)], label='x=y', c='black', linestyle='--')\n",
-    "plt.xlabel(r'interval width')\n",
+    "plt.xlabel(r'(min, max) width')\n",
     "plt.ylabel(r'interval width')\n",
-    "plt.legend()\n",
+    "plt.legend(markerscale=3)\n",
     "plt.tight_layout()\n",
-    "plt.savefig(f'{label}-auc-macc-interval-scatter.pdf')"
+    "plt.savefig(f'figures-intervals/{label}-auc-macc-interval-scatter.pdf')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 480,
+   "execution_count": 356,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Unnamed: 0', 'dataset', 'k', 'acc', 'sens', 'spec', 'auc', 'best_acc',\n",
-       "       'best_sens', 'best_spec', 'threshold', 'best_threshold',\n",
-       "       'best_acc_orig', 'p', 'n', 'auc_min', 'auc_min_best', 'auc_rmin',\n",
-       "       'auc_rmin_best', 'auc_amin', 'auc_amin_best', 'auc_armin',\n",
-       "       'auc_armin_best', 'auc_max', 'auc_max_best', 'auc_amax',\n",
-       "       'auc_amax_best', 'auc_maxa', 'auc_maxa_best', 'acc_min', 'acc_rmin',\n",
-       "       'acc_max', 'acc_rmax', 'max_acc_min', 'max_acc_max', 'max_acc_rmax',\n",
-       "       'auc_min_max', 'auc_rmin_max', 'auc_min_max_best', 'auc_rmin_max_best',\n",
-       "       'auc_min_maxa_best', 'auc_rmin_maxa_best', 'max_acc_min_max',\n",
-       "       'max_acc_min_rmax'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 480,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "data.columns"
+    "results.append({'target': ['auc', 'auc', 'auc', 'auc'],\n",
+    "                'source': ['fpr, tpr at max. acc.', 'fpr, tpr at max acc.', 'fpr, tpr at max acc.', 'fpr, tpr at max acc.'],\n",
+    "                'estimation': ['(min, max)', \n",
+    "                               '(rmin, max)',\n",
+    "                               '(min, maxa)',\n",
+    "                               '(rmin, maxa)'],\n",
+    "                'avg. lower': [np.mean(data['auc_min_best'] - data['auc']),\n",
+    "                               np.mean(data['auc_rmin_best'] - data['auc']),\n",
+    "                               np.mean(data['auc_min_best'] - data['auc']),\n",
+    "                               np.mean(data['auc_rmin_best'] - data['auc'])],\n",
+    "                'avg. upper': [np.mean(data['auc_max_best'] - data['auc']),\n",
+    "                              np.mean(data['auc_maxa_best'] - data['auc']),\n",
+    "                              np.mean(data['auc_max_best'] - data['auc']),\n",
+    "                              np.mean(data['auc_maxa_best'] - data['auc'])]})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 481,
+   "execution_count": 357,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 350x200 with 1 Axes>"
       ]
@@ -295,17 +311,17 @@
     "plt.ylabel('frequency')\n",
     "plt.legend()\n",
     "plt.tight_layout()\n",
-    "plt.savefig(f'{label}-max-acc-diffs-hist.pdf')"
+    "plt.savefig(f'figures-intervals/{label}-max-acc-diffs-hist.pdf')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 482,
+   "execution_count": 358,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 350x350 with 1 Axes>"
       ]
@@ -329,15 +345,41 @@
     "plt.ylabel(r'(min, rmax) width')\n",
     "plt.legend()\n",
     "plt.tight_layout()\n",
-    "plt.savefig(f'{label}-max-acc-interval-scatter.pdf')"
+    "plt.savefig(f'figures-intervals/{label}-max-acc-interval-scatter.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 359,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.append({'target': ['max. acc', 'max acc'],\n",
+    "                'source': ['auc', 'auc'],\n",
+    "                'estimation': ['(min, max)', '(min, rmax)'],\n",
+    "                'avg. lower': [np.mean(data['max_acc_min'] - data['best_acc']),\n",
+    "                               np.mean(data['max_acc_min'] - data['best_acc'])],\n",
+    "                'avg. upper': [np.mean(data['max_acc_max'] - data['best_acc']),\n",
+    "                              np.mean(data['max_acc_rmax'] - data['best_acc'])]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 360,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = pd.concat([pd.DataFrame(results[0]), pd.DataFrame(results[1]), pd.DataFrame(results[2])])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 361,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "results.to_csv(f'results-intervals-{label}.csv', index=False)"
+   ]
   }
  ],
  "metadata": {
diff --git a/notebooks/auc_experiments/05-results-midpoints-table.ipynb b/notebooks/auc_experiments/05-results-midpoints-table.ipynb
new file mode 100644
index 0000000..91f47bf
--- /dev/null
+++ b/notebooks/auc_experiments/05-results-midpoints-table.ipynb
@@ -0,0 +1,112 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = pd.read_csv('results-single.csv')\n",
+    "b = pd.read_csv('results-aggregated.csv')\n",
+    "c = pd.read_csv('results-aggregated-ns.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.concat([a, b[b.columns[3:]], c[c.columns[3:]]], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.columns = pd.MultiIndex.from_tuples([\n",
+    "    ('', 'target'),\n",
+    "    ('', 'source'),\n",
+    "    ('', 'estimation'),\n",
+    "    ('single test set', 'r2'),\n",
+    "    ('single test set', 'mape'),\n",
+    "    ('k-fold', 'r2'),\n",
+    "    ('k-fold', 'mape'),\n",
+    "    ('k-fold no strat.', 'r2'),\n",
+    "    ('k-fold no strat.', 'mape')]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{lllrrrrrr}\n",
+      "\\toprule\n",
+      "\\multicolumn{3}{r}{} & \\multicolumn{2}{r}{single test set} & \\multicolumn{2}{r}{k-fold} & \\multicolumn{2}{r}{k-fold no strat.} \\\\\n",
+      "target & source & estimation & r2 & mape & r2 & mape & r2 & mape \\\\\n",
+      "\\midrule\n",
+      "auc & arbitrary fpr, tpr & (min, max) & -1.605 & 0.247 & 0.029 & 0.122 & -0.002 & 0.124 \\\\\n",
+      "auc & arbitrary fpr, tpr & (rmin, max) & -0.289 & 0.131 & 0.436 & 0.057 & 0.411 & 0.057 \\\\\n",
+      "auc & fpr, tpr at max acc. & (min, max) & 0.814 & 0.064 & 0.629 & 0.083 & 0.603 & 0.085 \\\\\n",
+      "auc & fpr, tpr at max acc. & (rmin, max) & 0.789 & 0.059 & 0.687 & 0.080 & 0.678 & 0.081 \\\\\n",
+      "auc & fpr, tpr at max acc. & (min, maxa) & 0.621 & 0.067 & 0.300 & 0.123 & 0.222 & 0.123 \\\\\n",
+      "auc & fpr, tpr at max acc. & (rmin, maxa) & 0.854 & 0.040 & 0.752 & 0.070 & 0.743 & 0.068 \\\\\n",
+      "acc & auc & (min, max) & 0.848 & 0.039 & 0.901 & 0.030 & 0.894 & 0.031 \\\\\n",
+      "acc & auc & (min, rmax) & 0.898 & 0.032 & 0.924 & 0.027 & 0.916 & 0.028 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data.to_latex(index=False, float_format=\"%.3f\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mlscorecheck",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/auc_experiments/05-results-midpoints.ipynb b/notebooks/auc_experiments/05-results-midpoints.ipynb
index d90cf47..ffc43e2 100644
--- a/notebooks/auc_experiments/05-results-midpoints.ipynb
+++ b/notebooks/auc_experiments/05-results-midpoints.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 389,
+   "execution_count": 639,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15,23 +15,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 390,
+   "execution_count": 640,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#label = 'aggregated-ns'\n",
-    "#clabel = 'avg.'\n",
+    "label = 'aggregated-ns'\n",
+    "clabel = 'avg.'\n",
     "\n",
     "#label = 'aggregated'\n",
     "#clabel = 'avg.'\n",
     "\n",
-    "label = 'single'\n",
-    "clabel = ''"
+    "#label = 'single'\n",
+    "#clabel = ''"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 391,
+   "execution_count": 641,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 392,
+   "execution_count": 642,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,23 +49,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 393,
+   "execution_count": 643,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Index(['dataset', 'acc', 'sens', 'spec', 'auc', 'best_acc', 'best_sens',\n",
-       "       'best_spec', 'threshold', 'best_threshold', 'p', 'n', 'auc_min',\n",
-       "       'auc_min_best', 'auc_rmin', 'auc_rmin_best', 'auc_grmin',\n",
-       "       'auc_grmin_best', 'auc_amin', 'auc_amin_best', 'auc_armin',\n",
+       "Index(['Unnamed: 0', 'dataset', 'k', 'acc', 'sens', 'spec', 'auc', 'best_acc',\n",
+       "       'best_sens', 'best_spec', 'threshold', 'best_threshold',\n",
+       "       'best_acc_orig', 'p', 'n', 'auc_min', 'auc_min_best', 'auc_rmin',\n",
+       "       'auc_rmin_best', 'auc_amin', 'auc_amin_best', 'auc_armin',\n",
        "       'auc_armin_best', 'auc_max', 'auc_max_best', 'auc_amax',\n",
        "       'auc_amax_best', 'auc_maxa', 'auc_maxa_best', 'acc_min', 'acc_rmin',\n",
        "       'acc_max', 'acc_rmax', 'max_acc_min', 'max_acc_max', 'max_acc_rmax'],\n",
        "      dtype='object')"
       ]
      },
-     "execution_count": 393,
+     "execution_count": 643,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 394,
+   "execution_count": 644,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 395,
+   "execution_count": 645,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 396,
+   "execution_count": 646,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -119,12 +119,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 397,
+   "execution_count": 647,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 350x350 with 1 Axes>"
       ]
@@ -143,21 +143,21 @@
     "plt.plot([val_min, 1], [val_min, 1], label='x=y', c='black', linestyle='--')\n",
     "plt.legend(markerscale=4)\n",
     "plt.tight_layout()\n",
-    "plt.savefig(f'{label}-auc-midpoint.pdf')"
+    "plt.savefig(f'figures-midpoints/{label}-auc-midpoint.pdf')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 398,
+   "execution_count": 648,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(-1.6053968390213935, -0.0919572039971619)"
+       "(-0.0008138839362052952, 0.5577053576076464)"
       ]
      },
-     "execution_count": 398,
+     "execution_count": 648,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -170,16 +170,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 399,
+   "execution_count": 649,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(np.float64(0.24732478070850852), np.float64(0.13589534526842179))"
+       "(np.float64(0.12433225699566366), np.float64(0.09007135976727319))"
       ]
      },
-     "execution_count": 399,
+     "execution_count": 649,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -191,16 +191,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 400,
+   "execution_count": 650,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "WilcoxonResult(statistic=np.float64(3259741.5), pvalue=np.float64(0.0))"
+       "WilcoxonResult(statistic=np.float64(1928633.0), pvalue=np.float64(0.0))"
       ]
      },
-     "execution_count": 400,
+     "execution_count": 650,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -213,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 401,
+   "execution_count": 651,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -228,12 +228,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 402,
+   "execution_count": 652,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 350x350 with 1 Axes>"
       ]
@@ -259,21 +259,24 @@
     "plt.plot([val_min, 1], [val_min, 1], label='x=y', c='black', linestyle='--')\n",
     "plt.legend(markerscale=4, loc=(0.45, 0.05))\n",
     "plt.tight_layout()\n",
-    "plt.savefig(f'{label}-auc-macc-midpoint.pdf')"
+    "plt.savefig(f'figures-midpoints/{label}-auc-macc-midpoint.pdf')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 403,
+   "execution_count": 653,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.8141259390840927, 0.7894597446905978, 0.6211564466916335, 0.854063655995491)"
+       "(0.6013321551172672,\n",
+       " 0.6772722078779534,\n",
+       " 0.2236440856310249,\n",
+       " 0.7415562108182405)"
       ]
      },
-     "execution_count": 403,
+     "execution_count": 653,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -290,19 +293,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 404,
+   "execution_count": 654,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(np.float64(0.06436908659950068),\n",
-       " np.float64(0.05919203461799069),\n",
-       " np.float64(0.06712478248690905),\n",
-       " np.float64(0.040178145836855916))"
+       "(np.float64(0.08540121946597458),\n",
+       " np.float64(0.0813409722928719),\n",
+       " np.float64(0.12306933436142012),\n",
+       " np.float64(0.06888301937081791))"
       ]
      },
-     "execution_count": 404,
+     "execution_count": 654,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -316,16 +319,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 405,
+   "execution_count": 655,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "WilcoxonResult(statistic=np.float64(13607620.5), pvalue=np.float64(2.943419832221107e-76))"
+       "WilcoxonResult(statistic=np.float64(3277930.0), pvalue=np.float64(0.0))"
       ]
      },
-     "execution_count": 405,
+     "execution_count": 655,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -338,7 +341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 406,
+   "execution_count": 656,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -357,12 +360,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 407,
+   "execution_count": 657,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 350x350 with 1 Axes>"
       ]
@@ -378,26 +381,26 @@
     "              min(data['max_acc_min_rmax']))\n",
     "plt.scatter(data['best_acc'], data['max_acc_min_max'], label='(min, max)', s=3)\n",
     "plt.scatter(data['best_acc'], data['max_acc_min_rmax'], label='(min, rmax)', s=3)\n",
-    "plt.xlabel(f'{clabel} acc')\n",
-    "plt.ylabel(f'{clabel} acc midpoint estimation')\n",
+    "plt.xlabel(f'{clabel} max. acc.')\n",
+    "plt.ylabel(f'{clabel} max. acc. midpoint estimation')\n",
     "plt.plot([val_min, 1], [val_min, 1], label='x=y', c='black', linestyle='--')\n",
     "plt.legend(markerscale=4)\n",
     "plt.tight_layout()\n",
-    "plt.savefig(f'{label}-max-acc-midpoint.pdf')"
+    "plt.savefig(f'figures-midpoints/{label}-max-acc-midpoint.pdf')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 408,
+   "execution_count": 658,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.8476790115040671, 0.8983914582015115)"
+       "(0.8944196326981846, 0.9164001606936467)"
       ]
      },
-     "execution_count": 408,
+     "execution_count": 658,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -411,16 +414,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 409,
+   "execution_count": 659,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(np.float64(0.03928668545601988), np.float64(0.032186892637278114))"
+       "(np.float64(0.031013479213606737), np.float64(0.027843963675247017))"
       ]
      },
-     "execution_count": 409,
+     "execution_count": 659,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -432,16 +435,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 410,
+   "execution_count": 660,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "WilcoxonResult(statistic=np.float64(7015405.0), pvalue=np.float64(4.5634722317954595e-174))"
+       "WilcoxonResult(statistic=np.float64(4898053.0), pvalue=np.float64(3.606946845628045e-147))"
       ]
      },
-     "execution_count": 410,
+     "execution_count": 660,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -454,11 +457,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 411,
+   "execution_count": 661,
    "metadata": {},
    "outputs": [],
    "source": [
-    "results.append({'target': ['acc', 'acc'],\n",
+    "results.append({'target': ['max. acc.', 'max. acc.'],\n",
     "                'source': ['auc', 'auc'],\n",
     "                'estimation': ['(min, max)', '(min, rmax)'],\n",
     "                'r2': (r2_score(tmp0['best_acc'], tmp0['max_acc_min_max']),\n",
@@ -469,7 +472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 412,
+   "execution_count": 662,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -478,11 +481,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 413,
+   "execution_count": 663,
    "metadata": {},
    "outputs": [],
    "source": [
-    "results.to_csv(f'results-{label}.csv', index=False)"
+    "results.to_csv(f'results-midpoints-{label}.csv', index=False)"
    ]
   },
   {
diff --git a/notebooks/auc_experiments/05-results-table.ipynb b/notebooks/auc_experiments/05-results-table.ipynb
deleted file mode 100644
index 20e2b9b..0000000
--- a/notebooks/auc_experiments/05-results-table.ipynb
+++ /dev/null
@@ -1,258 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = pd.read_csv('results-single.csv')\n",
-    "b = pd.read_csv('results-aggregated.csv')\n",
-    "c = pd.read_csv('results-aggregated-ns.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.concat([a, b[b.columns[3:]], c[c.columns[3:]]], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data.columns = pd.MultiIndex.from_tuples([\n",
-    "    ('', 'target'),\n",
-    "    ('', 'source'),\n",
-    "    ('', 'estimation'),\n",
-    "    ('single test set', 'r2'),\n",
-    "    ('single test set', 'mape'),\n",
-    "    ('k-fold', 'r2'),\n",
-    "    ('k-fold', 'mape'),\n",
-    "    ('k-fold no strat.', 'r2'),\n",
-    "    ('k-fold no strat.', 'mape')]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th colspan=\"3\" halign=\"left\"></th>\n",
-       "      <th colspan=\"2\" halign=\"left\">single test set</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">k-fold</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">k-fold no strat.</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>target</th>\n",
-       "      <th>source</th>\n",
-       "      <th>estimation</th>\n",
-       "      <th>r2</th>\n",
-       "      <th>mape</th>\n",
-       "      <th>r2</th>\n",
-       "      <th>mape</th>\n",
-       "      <th>r2</th>\n",
-       "      <th>mape</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>auc</td>\n",
-       "      <td>arbitrary fpr, tpr</td>\n",
-       "      <td>(min, max)</td>\n",
-       "      <td>-1.605397</td>\n",
-       "      <td>0.247325</td>\n",
-       "      <td>0.029479</td>\n",
-       "      <td>0.122130</td>\n",
-       "      <td>-0.001573</td>\n",
-       "      <td>0.124058</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>auc</td>\n",
-       "      <td>arbitrary fpr, tpr</td>\n",
-       "      <td>(rmin, max)</td>\n",
-       "      <td>-0.288562</td>\n",
-       "      <td>0.131031</td>\n",
-       "      <td>0.435546</td>\n",
-       "      <td>0.057205</td>\n",
-       "      <td>0.410942</td>\n",
-       "      <td>0.056522</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>auc</td>\n",
-       "      <td>fpr, tpr at max acc.</td>\n",
-       "      <td>(min, max)</td>\n",
-       "      <td>0.814126</td>\n",
-       "      <td>0.064369</td>\n",
-       "      <td>0.628899</td>\n",
-       "      <td>0.082540</td>\n",
-       "      <td>0.602903</td>\n",
-       "      <td>0.085201</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>auc</td>\n",
-       "      <td>fpr, tpr at max acc.</td>\n",
-       "      <td>(rmin, max)</td>\n",
-       "      <td>0.789460</td>\n",
-       "      <td>0.059192</td>\n",
-       "      <td>0.686994</td>\n",
-       "      <td>0.079595</td>\n",
-       "      <td>0.677889</td>\n",
-       "      <td>0.081255</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>auc</td>\n",
-       "      <td>fpr, tpr at max acc.</td>\n",
-       "      <td>(min, maxa)</td>\n",
-       "      <td>0.621156</td>\n",
-       "      <td>0.067125</td>\n",
-       "      <td>0.300404</td>\n",
-       "      <td>0.122629</td>\n",
-       "      <td>0.222056</td>\n",
-       "      <td>0.123042</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>auc</td>\n",
-       "      <td>fpr, tpr at max acc</td>\n",
-       "      <td>(rmin, maxa)</td>\n",
-       "      <td>0.854064</td>\n",
-       "      <td>0.040178</td>\n",
-       "      <td>0.751567</td>\n",
-       "      <td>0.070350</td>\n",
-       "      <td>0.742680</td>\n",
-       "      <td>0.068468</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>acc</td>\n",
-       "      <td>auc</td>\n",
-       "      <td>(min, max)</td>\n",
-       "      <td>0.847679</td>\n",
-       "      <td>0.039287</td>\n",
-       "      <td>0.901321</td>\n",
-       "      <td>0.030477</td>\n",
-       "      <td>0.894388</td>\n",
-       "      <td>0.031048</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>acc</td>\n",
-       "      <td>auc</td>\n",
-       "      <td>(min, rmax)</td>\n",
-       "      <td>0.898391</td>\n",
-       "      <td>0.032187</td>\n",
-       "      <td>0.923561</td>\n",
-       "      <td>0.027078</td>\n",
-       "      <td>0.916388</td>\n",
-       "      <td>0.027874</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                             single test set            \\\n",
-       "  target                source    estimation              r2      mape   \n",
-       "0    auc    arbitrary fpr, tpr    (min, max)       -1.605397  0.247325   \n",
-       "1    auc    arbitrary fpr, tpr   (rmin, max)       -0.288562  0.131031   \n",
-       "2    auc  fpr, tpr at max acc.    (min, max)        0.814126  0.064369   \n",
-       "3    auc  fpr, tpr at max acc.   (rmin, max)        0.789460  0.059192   \n",
-       "4    auc  fpr, tpr at max acc.   (min, maxa)        0.621156  0.067125   \n",
-       "5    auc   fpr, tpr at max acc  (rmin, maxa)        0.854064  0.040178   \n",
-       "6    acc                   auc    (min, max)        0.847679  0.039287   \n",
-       "7    acc                   auc   (min, rmax)        0.898391  0.032187   \n",
-       "\n",
-       "     k-fold           k-fold no strat.            \n",
-       "         r2      mape               r2      mape  \n",
-       "0  0.029479  0.122130        -0.001573  0.124058  \n",
-       "1  0.435546  0.057205         0.410942  0.056522  \n",
-       "2  0.628899  0.082540         0.602903  0.085201  \n",
-       "3  0.686994  0.079595         0.677889  0.081255  \n",
-       "4  0.300404  0.122629         0.222056  0.123042  \n",
-       "5  0.751567  0.070350         0.742680  0.068468  \n",
-       "6  0.901321  0.030477         0.894388  0.031048  \n",
-       "7  0.923561  0.027078         0.916388  0.027874  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "mlscorecheck",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/auc_experiments/00-intervals-acc.ipynb b/notebooks/auc_experiments/xx-00-intervals-acc.ipynb
similarity index 100%
rename from notebooks/auc_experiments/00-intervals-acc.ipynb
rename to notebooks/auc_experiments/xx-00-intervals-acc.ipynb
diff --git a/notebooks/auc_experiments/00_intervals-auc-kfold.ipynb b/notebooks/auc_experiments/xx-00_intervals-auc-kfold.ipynb
similarity index 100%
rename from notebooks/auc_experiments/00_intervals-auc-kfold.ipynb
rename to notebooks/auc_experiments/xx-00_intervals-auc-kfold.ipynb
diff --git a/notebooks/auc_experiments/00_intervals-auc.ipynb b/notebooks/auc_experiments/xx-00_intervals-auc.ipynb
similarity index 100%
rename from notebooks/auc_experiments/00_intervals-auc.ipynb
rename to notebooks/auc_experiments/xx-00_intervals-auc.ipynb