I've this code to print the importance of each variable on my model:
importances = trained_model.feature_importances_ std = np.std([trained_model.feature_importances_ for trained_model in trained_model.estimators_], axis=0) indices = np.argsort(importances)[::-1] # Print the feature ranking print("Feature ranking:") for f in range(training_features.shape): print("%d. feature %d (%f)" % (f + 1, indices[f], importances[indices[f]]))
I print a lot of variables with feature ranking as 0.0. Should I remove that variables? I can I do it using Python?
df = df.drop('Col_A', 1) WHERE importances[indices[f]] = 0