Python for Finance Cookbook

How to do it...

Execute the following steps to evaluate the feature importance of a Random Forest model.

1. Import the libraries:

from sklearn.base import clone from eli5.sklearn import PermutationImportance

2. Extract the classifier and preprocessor from the pipeline:

rf_classifier = rf_pipeline.named_steps['classifier']preprocessor = rf_pipeline.named_steps['preprocessor']

3. Recover feature names from the preprocessing transformer, and transform the training data:

feat_names = preprocessor.named_transformers_['categorical'] \
                         .named_steps['onehot'] \
                         .get_feature_names(    input_features=cat_features)
feat_names = np.r_[num_features, feat_names]

X_train_preprocessed = pd.DataFrame(    preprocessor.transform(X_train), 
    columns=feat_names)

4. Extract ...