Computing feature importance with decision trees
You built a decision tree classifier to identify patients at risk of heart disease using the heart disease dataset. Now you need to explain the model by analyzing feature importance to determine the key factors for predicting heart disease, enabling more targeted healthcare interventions.
matplotlib.pyplot has been imported as plt. X_train and y_train are pre-loaded for you.
This exercise is part of the course
Explainable AI in Python
Exercise instructions
- Extract the feature importances from the
model. - Plot the
feature_importancesfor the givenfeature_names.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
model = DecisionTreeClassifier(random_state=42)
model.fit(X_train, y_train)
# Derive feature importances
feature_importances = ____
feature_names = X_train.columns
# Plot the feature importances
____
plt.show()