What is a common method for feature importance evaluation in Random Forests?
Practice Questions
Q1
What is a common method for feature importance evaluation in Random Forests?
Permutation importance
Gradient boosting
K-fold cross-validation
Principal component analysis
Questions & Step-by-Step Solutions
What is a common method for feature importance evaluation in Random Forests?
Step 1: Train a Random Forest model using your dataset with all features.
Step 2: Measure the model's accuracy or prediction error on a validation set. This is your baseline performance.
Step 3: Choose one feature that you want to evaluate for importance.
Step 4: Randomly shuffle the values of that chosen feature in the validation set. This means you mix up the values so they no longer correspond to the original data.
Step 5: Use the Random Forest model to make predictions again using the modified validation set (with the shuffled feature).
Step 6: Measure the new accuracy or prediction error of the model with the shuffled feature.
Step 7: Compare the new prediction error with the baseline performance from Step 2.
Step 8: The difference in prediction error indicates the importance of the feature: a larger increase in error means the feature is more important.
