Metadata-Version: 2.1
Name: shap-selection
Version: 0.1.5
Summary: Selecting features using SHAP values
Home-page: https://github.com/wilsonjr/SHAP_FSelection
Author: Wilson Estecio Marcilio Junior
Author-email: wilson_jr@outlook.com
License: MIT
Description: # SHAP-Selection: Selecting feature using SHAP values
        
        Due to the increasing concerns about machine learning interpretability, we believe that interpretation could be added to pre-processing steps. Using this library, you will be able to select the most important features from a multidimensional dataset while being able to explain your decisions!
        
        To use SHAP-Selection, you will need:
          * [SHAP](https://github.com/slundberg/shap)
        
        ## Instalation
        ```
        pip install shap-selection
        ```
        
        ## Citation
        
        ```BibTex
        @INPROCEEDINGS{MarcilioJr2020shapselection,  
          author={W. E. {MarcÃ­lio} and D. M. {Eler}}, 
          booktitle={2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)},   
          title={From explanations to feature selection: assessing SHAP values as feature selection mechanism},   
          year={2020},  
          pages={340-347},  
          doi={10.1109/SIBGRAPI51738.2020.00053}
        }
        ```
        
        ## Usage 
        
        To use SHAP-Selection, you must have a trained model. It works both for classification and regression purposes!
        
        ##### Load a dataset
        
        ```python
        iris_data = load_iris()
        
        X, y = iris_data.data, iris_data.target
        feature_names = np.array(iris_data.feature_names)
        
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
        ```
        
        ##### Fit a model
        
        ```python
        model = cb.CatBoostClassifier(verbose=False)    
        model.fit(X_train, y_train)
        ```
        
        ##### Use SHAP-Selection
        
        ```python
        
        from shap_selection import feature_selection
        
        # please, use agnostic = True to use with any model...
        # agnostic = True will only work with tree-based models
        feature_order = feature_selection.shap_select(model, X_train, X_test, feature_names, agnostic=False)
        ```
        
        ### Support 
        
        Please, if you have any questions feel free to contact me at wilson_jr@outlook.com
        
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