Computing feature importance with random forests
As a data scientist at a financial consulting firm, you have developed a random forest classifier that classifies individuals according to their income levels. Now, you need to explain the model by analyzing feature importance to determine the key factors for predicting income, enabling more targeted market segmentation and improving strategic decision-making.
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_importances.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)
# Derive feature importances
feature_importances = ____
# Plot the feature importances
____
plt.show()