9- CART  Model - Tahmin - Doğrulama.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kütüphaneler\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split , GridSearchCV\n",
    "from sklearn.metrics import mean_squared_error , r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import scale     # standardizasoyon işlemleri\n",
    "from sklearn.preprocessing import StandardScaler    # standardizasoyon işlemleri\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn import neighbors\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "\n",
    "from warnings import filterwarnings\n",
    "filterwarnings(\"ignore\")     # önceki bölümlerde çalıştırdığımızda çıkan gereksiz uyarı mesajları çıkmasın diye"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:\\python_py\\Makine Öğrenmesi-2\\Hitters.csv\")\n",
    "df = df.dropna()\n",
    "\n",
    "dms = pd.get_dummies(df[[\"League\" , \"Division\" , \"NewLeague\"]])\n",
    "\n",
    "y = df[\"Salary\"]\n",
    "\n",
    "x__ = df.drop([\"Salary\" , \"League\" , \"Division\" , \"NewLeague\"] , axis=1).astype(\"float64\")\n",
    "x = pd.concat([x__ , dms[[\"League_N\" , \"Division_W\" , \"NewLeague_N\"]]] , axis=1)\n",
    "\n",
    "x_train , x_test , y_train , y_test = train_test_split(x , y , test_size=0.25 , random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Yeni olarak ağaca dayalı bir algoritmaya geçtiğimiz için iki değişken üzerinden işlem yapacağız. Bu değişkenler üzerinden anlamaya çalışalım.\n",
    "\n",
    "x_train = pd.DataFrame(x_train[\"Hits\"])\n",
    "x_test = pd.DataFrame(x_test[\"Hits\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "cart_model = DecisionTreeRegressor().fit(x_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Maaş (Salary)')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Görsel olarak arka taraftaki ağaç yapısının nasıl olduğuna göz atalım\n",
    "\n",
    "x_grid = np.arange(min(np.array(x_train)) , max(np.array(x_train)) , 0.01)\n",
    "x_grid = x_grid.reshape((len(x_grid)) , 1)\n",
    "\n",
    "plt.scatter(x_train , y_train , color=\"red\")\n",
    "\n",
    "plt.plot(x_grid , cart_model.predict(x_grid) , color=\"blue\")\n",
    "\n",
    "plt.title(\"CART Regresyon Ağacı\")\n",
    "plt.xlabel(\"Atış Sayısı (Hits)\")\n",
    "plt.ylabel(\"Maaş (Salary)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TAHMİN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 725.    , 1230.    ,  725.    ,  504.1665,   67.5   ])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cart_model.predict(x_test)[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "468.49579052913884"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test setinin TEK DEĞİŞKENLİ (Hits) ilk hata oranına bakalım\n",
    "\"\"\"\n",
    "y_pred = cart_model.predict(x_test)\n",
    "\n",
    "RMSE = np.sqrt(mean_squared_error(y_test , y_pred))\n",
    "RMSE \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "465.7854115476256"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test setinin TÜM DEĞİŞKENLİ ilk hata oranına bakalım\n",
    "\n",
    "df = pd.read_csv(\"C:\\python_py\\Makine Öğrenmesi-2\\Hitters.csv\")\n",
    "df = df.dropna()\n",
    "\n",
    "dms = pd.get_dummies(df[[\"League\" , \"Division\" , \"NewLeague\"]])\n",
    "\n",
    "y = df[\"Salary\"]\n",
    "\n",
    "x__ = df.drop([\"Salary\" , \"League\" , \"Division\" , \"NewLeague\"] , axis=1).astype(\"float64\")\n",
    "x = pd.concat([x__ , dms[[\"League_N\" , \"Division_W\" , \"NewLeague_N\"]]] , axis=1)\n",
    "\n",
    "x_train , x_test , y_train , y_test = train_test_split(x , y , test_size=0.25 , random_state=42)\n",
    "\n",
    "cart_model = DecisionTreeRegressor().fit(x_train , y_train)\n",
    "\n",
    "y_pred = cart_model.predict(x_test)\n",
    "\n",
    "RMSE = np.sqrt(mean_squared_error(y_test , y_pred))\n",
    "RMSE\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Görüldüğü üzere yeni değişkenler eklendikçe daha başarılı tahmin elde edilebiliyor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODEL TUNING (DOĞRULAMA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;31mType:\u001b[0m        DecisionTreeRegressor\n",
      "\u001b[1;31mString form:\u001b[0m DecisionTreeRegressor()\n",
      "\u001b[1;31mFile:\u001b[0m        c:\\users\\bedirhan örseloğlu\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages\\sklearn\\tree\\_classes.py\n",
      "\u001b[1;31mDocstring:\u001b[0m  \n",
      "A decision tree regressor.\n",
      "\n",
      "Read more in the :ref:`User Guide <tree>`.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "criterion : {\"squared_error\", \"friedman_mse\", \"absolute_error\",             \"poisson\"}, default=\"squared_error\"\n",
      "    The function to measure the quality of a split. Supported criteria\n",
      "    are \"squared_error\" for the mean squared error, which is equal to\n",
      "    variance reduction as feature selection criterion and minimizes the L2\n",
      "    loss using the mean of each terminal node, \"friedman_mse\", which uses\n",
      "    mean squared error with Friedman's improvement score for potential\n",
      "    splits, \"absolute_error\" for the mean absolute error, which minimizes\n",
      "    the L1 loss using the median of each terminal node, and \"poisson\" which\n",
      "    uses reduction in Poisson deviance to find splits.\n",
      "\n",
      "    .. versionadded:: 0.18\n",
      "       Mean Absolute Error (MAE) criterion.\n",
      "\n",
      "    .. versionadded:: 0.24\n",
      "        Poisson deviance criterion.\n",
      "\n",
      "splitter : {\"best\", \"random\"}, default=\"best\"\n",
      "    The strategy used to choose the split at each node. Supported\n",
      "    strategies are \"best\" to choose the best split and \"random\" to choose\n",
      "    the best random split.\n",
      "\n",
      "max_depth : int, default=None\n",
      "    The maximum depth of the tree. If None, then nodes are expanded until\n",
      "    all leaves are pure or until all leaves contain less than\n",
      "    min_samples_split samples.\n",
      "\n",
      "min_samples_split : int or float, default=2\n",
      "    The minimum number of samples required to split an internal node:\n",
      "\n",
      "    - If int, then consider `min_samples_split` as the minimum number.\n",
      "    - If float, then `min_samples_split` is a fraction and\n",
      "      `ceil(min_samples_split * n_samples)` are the minimum\n",
      "      number of samples for each split.\n",
      "\n",
      "    .. versionchanged:: 0.18\n",
      "       Added float values for fractions.\n",
      "\n",
      "min_samples_leaf : int or float, default=1\n",
      "    The minimum number of samples required to be at a leaf node.\n",
      "    A split point at any depth will only be considered if it leaves at\n",
      "    least ``min_samples_leaf`` training samples in each of the left and\n",
      "    right branches.  This may have the effect of smoothing the model,\n",
      "    especially in regression.\n",
      "\n",
      "    - If int, then consider `min_samples_leaf` as the minimum number.\n",
      "    - If float, then `min_samples_leaf` is a fraction and\n",
      "      `ceil(min_samples_leaf * n_samples)` are the minimum\n",
      "      number of samples for each node.\n",
      "\n",
      "    .. versionchanged:: 0.18\n",
      "       Added float values for fractions.\n",
      "\n",
      "min_weight_fraction_leaf : float, default=0.0\n",
      "    The minimum weighted fraction of the sum total of weights (of all\n",
      "    the input samples) required to be at a leaf node. Samples have\n",
      "    equal weight when sample_weight is not provided.\n",
      "\n",
      "max_features : int, float or {\"auto\", \"sqrt\", \"log2\"}, default=None\n",
      "    The number of features to consider when looking for the best split:\n",
      "\n",
      "    - If int, then consider `max_features` features at each split.\n",
      "    - If float, then `max_features` is a fraction and\n",
      "      `max(1, int(max_features * n_features_in_))` features are considered at each\n",
      "      split.\n",
      "    - If \"sqrt\", then `max_features=sqrt(n_features)`.\n",
      "    - If \"log2\", then `max_features=log2(n_features)`.\n",
      "    - If None, then `max_features=n_features`.\n",
      "\n",
      "    Note: the search for a split does not stop until at least one\n",
      "    valid partition of the node samples is found, even if it requires to\n",
      "    effectively inspect more than ``max_features`` features.\n",
      "\n",
      "random_state : int, RandomState instance or None, default=None\n",
      "    Controls the randomness of the estimator. The features are always\n",
      "    randomly permuted at each split, even if ``splitter`` is set to\n",
      "    ``\"best\"``. When ``max_features < n_features``, the algorithm will\n",
      "    select ``max_features`` at random at each split before finding the best\n",
      "    split among them. But the best found split may vary across different\n",
      "    runs, even if ``max_features=n_features``. That is the case, if the\n",
      "    improvement of the criterion is identical for several splits and one\n",
      "    split has to be selected at random. To obtain a deterministic behaviour\n",
      "    during fitting, ``random_state`` has to be fixed to an integer.\n",
      "    See :term:`Glossary <random_state>` for details.\n",
      "\n",
      "max_leaf_nodes : int, default=None\n",
      "    Grow a tree with ``max_leaf_nodes`` in best-first fashion.\n",
      "    Best nodes are defined as relative reduction in impurity.\n",
      "    If None then unlimited number of leaf nodes.\n",
      "\n",
      "min_impurity_decrease : float, default=0.0\n",
      "    A node will be split if this split induces a decrease of the impurity\n",
      "    greater than or equal to this value.\n",
      "\n",
      "    The weighted impurity decrease equation is the following::\n",
      "\n",
      "        N_t / N * (impurity - N_t_R / N_t * right_impurity\n",
      "                            - N_t_L / N_t * left_impurity)\n",
      "\n",
      "    where ``N`` is the total number of samples, ``N_t`` is the number of\n",
      "    samples at the current node, ``N_t_L`` is the number of samples in the\n",
      "    left child, and ``N_t_R`` is the number of samples in the right child.\n",
      "\n",
      "    ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,\n",
      "    if ``sample_weight`` is passed.\n",
      "\n",
      "    .. versionadded:: 0.19\n",
      "\n",
      "ccp_alpha : non-negative float, default=0.0\n",
      "    Complexity parameter used for Minimal Cost-Complexity Pruning. The\n",
      "    subtree with the largest cost complexity that is smaller than\n",
      "    ``ccp_alpha`` will be chosen. By default, no pruning is performed. See\n",
      "    :ref:`minimal_cost_complexity_pruning` for details.\n",
      "\n",
      "    .. versionadded:: 0.22\n",
      "\n",
      "Attributes\n",
      "----------\n",
      "feature_importances_ : ndarray of shape (n_features,)\n",
      "    The feature importances.\n",
      "    The higher, the more important the feature.\n",
      "    The importance of a feature is computed as the\n",
      "    (normalized) total reduction of the criterion brought\n",
      "    by that feature. It is also known as the Gini importance [4]_.\n",
      "\n",
      "    Warning: impurity-based feature importances can be misleading for\n",
      "    high cardinality features (many unique values). See\n",
      "    :func:`sklearn.inspection.permutation_importance` as an alternative.\n",
      "\n",
      "max_features_ : int\n",
      "    The inferred value of max_features.\n",
      "\n",
      "n_features_in_ : int\n",
      "    Number of features seen during :term:`fit`.\n",
      "\n",
      "    .. versionadded:: 0.24\n",
      "\n",
      "feature_names_in_ : ndarray of shape (`n_features_in_`,)\n",
      "    Names of features seen during :term:`fit`. Defined only when `X`\n",
      "    has feature names that are all strings.\n",
      "\n",
      "    .. versionadded:: 1.0\n",
      "\n",
      "n_outputs_ : int\n",
      "    The number of outputs when ``fit`` is performed.\n",
      "\n",
      "tree_ : Tree instance\n",
      "    The underlying Tree object. Please refer to\n",
      "    ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and\n",
      "    :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`\n",
      "    for basic usage of these attributes.\n",
      "\n",
      "See Also\n",
      "--------\n",
      "DecisionTreeClassifier : A decision tree classifier.\n",
      "\n",
      "Notes\n",
      "-----\n",
      "The default values for the parameters controlling the size of the trees\n",
      "(e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and\n",
      "unpruned trees which can potentially be very large on some data sets. To\n",
      "reduce memory consumption, the complexity and size of the trees should be\n",
      "controlled by setting those parameter values.\n",
      "\n",
      "References\n",
      "----------\n",
      "\n",
      ".. [1] https://en.wikipedia.org/wiki/Decision_tree_learning\n",
      "\n",
      ".. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, \"Classification\n",
      "       and Regression Trees\", Wadsworth, Belmont, CA, 1984.\n",
      "\n",
      ".. [3] T. Hastie, R. Tibshirani and J. Friedman. \"Elements of Statistical\n",
      "       Learning\", Springer, 2009.\n",
      "\n",
      ".. [4] L. Breiman, and A. Cutler, \"Random Forests\",\n",
      "       https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm\n",
      "\n",
      "Examples\n",
      "--------\n",
      ">>> from sklearn.datasets import load_diabetes\n",
      ">>> from sklearn.model_selection import cross_val_score\n",
      ">>> from sklearn.tree import DecisionTreeRegressor\n",
      ">>> X, y = load_diabetes(return_X_y=True)\n",
      ">>> regressor = DecisionTreeRegressor(random_state=0)\n",
      ">>> cross_val_score(regressor, X, y, cv=10)\n",
      "...                    # doctest: +SKIP\n",
      "...\n",
      "array([-0.39..., -0.46...,  0.02...,  0.06..., -0.50...,\n",
      "       0.16...,  0.11..., -0.73..., -0.30..., -0.00...])"
     ]
    }
   ],
   "source": [
    "# CART'ın hiperparametrelerini text editor üzerinden inceledik.\n",
    "\n",
    "?cart_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "cart_params = {\"max_depth\":[2 , 3 , 4 , 5 , 10 , 20] , \"min_samples_split\":[2 , 10 , 5 , 30 , 50 , 100]}\n",
    "# en önemli parametre min_samples_split !!!!!!!!!!!!!!!\n",
    "\n",
    "cart_model = DecisionTreeRegressor()\n",
    "cart_cv_model = GridSearchCV(cart_model , cart_params , cv=10).fit(x_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 4, 'min_samples_split': 50}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cart_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FİNAL MODELİ\n",
    "\n",
    "cart_model_tuned = DecisionTreeRegressor(max_depth=4 , min_samples_split=50).fit(x_train , y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "411.9886403043614"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test setimizin son hata oranı\n",
    "\n",
    "y_pred = cart_model_tuned.predict(x_test)\n",
    "\n",
    "RMSE = np.sqrt(mean_squared_error(y_pred , y_test))\n",
    "RMSE\n",
    "\n",
    "# İlk hata oranımız 465.7854115476256'idi"
   ]
  }
 ],
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