Feature Importance
Explore how to determine the importance of features in random forest models using permutation importance techniques. Understand the process of shuffling feature values, measuring impacts on prediction accuracy, and visualizing results with R's randomForest package. This lesson teaches you to identify which variables most influence model performance and supports better feature engineering decisions.
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Finding the features that matter
When using machine learning, it’s natural to ask, “Which features are the most important for making accurate predictions?” The random forest implements permutation importance to help answer this question. Permutation importance works by randomly shuffling (permuting) feature data and assessing the impact of the shuffling on the quality of predictions.
Here’s the intuition of permutation importance:
If you permute the values of highly predictive features, tree accuracy should decrease a lot.
If you permute the values of features that aren’t predictive, tree accuracy shouldn’t decrease much.
Imagine the worst feature possible —a set of completely random values. Theoretically, tree accuracy would not decrease if you permute the feature values. ...