Feature Importance plots for admissions analysis
As part of the data science team at a university, your task is to evaluate which factors truly drive admissions decisions and which ones are less important. While the admissions committee knows CGPA plays a key role, they want to confirm this and uncover any other important factors that may influence outcomes. Using a RandomForestRegressor model, you'll visualize feature importance to clearly identify which aspects of applicants' profiles matter most and which have less of an impact on the decision process.
The shap library and the training data (X_train, y_train) have been pre-loaded for you.
This exercise is part of the course
Explainable AI in Python
Exercise instructions
- Derive the
shap_valuesusing aTreeExplainer. - Use the derived
shap_valuesto plot the feature importances with a bar plot and analyze it.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
model = RandomForestRegressor(random_state=42)
model.fit(X_train, y_train)
# Derive shap values
explainer = ____
shap_values = ____
# Plot the feature importance plot
____