From 440faf0e0ce4e322431c804411103fd487b318a5 Mon Sep 17 00:00:00 2001 From: Ziqi Li Date: Sun, 14 Apr 2024 09:33:28 -0400 Subject: [PATCH] Update README.md --- README.md | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index af3b3ee..7b9f3ec 100644 --- a/README.md +++ b/README.md @@ -21,9 +21,20 @@ $ pip install geoshapley ### Example: -GeoShapley can explain any model that takes data + coordiantes as the input. Currently, coordinates need to be put as the last two columns of your `pandas.DataFrame`(`X_geo`). +GeoShapley can explain any model that takes tabular data + spatial features (e.g., coordinates) as the input. Examples of natively supported models include: +1. XGBoost/CatBoost/LightGBM +2. Random Forest +3. MLP or other `scikit-learn` modules. +4. [TabNet](https://github.com/dreamquark-ai/tabnet) +6. [Explainable Boosting Machine](https://github.com/interpretml/interpret) +7. Statistical models: OLS/Gaussian Process/GWR -Below shows an example on how to explain a trained NN model. +Other models can be supported by defining a helper function model.predict() to wrap around their original models' prediction or inference functions. + +Currently, spatial features (e.g., coordinates) need to be put as the last columns of your `pandas.DataFrame`(`X_geo`). + + +Below shows an example on how to explain a trained MLP model. More examples can be found at the notebooks folder. ```python from geoshapley import GeoShapleyExplainer