Selecting important features

In this exercise, your task is to select only the most important features that will be used by the final model. Remember, that the relative importances are saved in the column importance of the DataFrame called relative_importances.

Este exercício faz parte do curso

HR Analytics: Predicting Employee Churn in Python

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Instruções do exercício

Select only the features with an importance value higher than 1%.
Create a list from those features and print them (this has been done for you).
Using the index saved in selected_list, transform both features_train and features_test to include the features with an importance higher than 1% only.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# select only features with relative importance higher than 1%
selected_features = relative_importances[relative_importances.____>0.01]

# create a list from those features: done
selected_list = selected_features.index

# transform both features_train and features_test components to include only selected features
features_train_selected = features_train[selected_list]
features_test_selected = ____[____]

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