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I'm using pipeline to transform data and predict model and I want to apply SHAP after that. However, when I apply it, it returns SHAP chart just fine, but the name of the feature are like feature 1, feature 2, etc - like the image bellow.

How can I get the real feature name in shap?

My code:

def pipeline(categoricas_all,numericas_all, model):
    encoder = OneHotEncoder() #apenas para categoricas com baixa cardinalidade

    imputer_num = SimpleImputer(strategy = 'median')
    imputer_cat = SimpleImputer(strategy = 'most_frequent')
    
    numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy = 'median'))
    # ,('scaler', StandardScaler())
    ])

    categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy = 'most_frequent'))
    ,('encod', encoder)
    ])

    preprocessor = ColumnTransformer(
     transformers=[
    ('num', numeric_transformer, numericas_all)
    ,('cat', categorical_transformer, categoricas_all)
    ])
    
    pipe = Pipeline(steps=[('preprocessor', preprocessor),('classifier', model)])
    return preprocessor, pipe


processor, pipe = pipeline(categoricas_all,numericas_all, item)
pipe.fit(X_train, y_train)

explainer = shap.Explainer(pipe["classifier"])
data_transformation = pipe['preprocessor'].transform(X_test)

shap_values = explainer(data_transformation)

shap.plots.waterfall(shap_values[0])

enter image description here

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1 Answer 1

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probably a bit late, but still.

In sklearn, Pipeline/ColumnTransformer (and other) have usually function get_feature_names_out() returning feature names after transformation (so matching the shape of transformed data) and shap.Explainer takes feature_names as argument, so in your case:

explainer = shap.Explainer(pipe["classifier"], feature_names=processor.get_feature_names_out())
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