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It’s important to keep this in mind when interpreting feature importance plots, as it can lead to incorrect conclusions about the importance of features in the context of the model. However, when a feature has a high number of unique values, the gain in impurity reduction is artificially inflated due to the fact that the model is able to split on the feature more often. It happens because the algorithm uses the gain in impurity reduction as a proxy for feature importance. This bias is a common problem in Random Forest models, where the model tends to overestimate the importance of features with a high number of unique values. High Cardinality Bias In Random Forests #īefore we dive into the code, it’s important to understand the high cardinality bias. How Does Data Leakage Affect Feature Importances In Random Forests?.Can I Use The Same Techniques For Other Ensemble Models Like Xgboost?.How Can I Use The Results Of The Feature Importance Analysis To Improve My Model Performance?.Can The Techniques Described In This Tutorial Be Applied To Classification Problems?.Additional Questions About Feature Importance In Random Forests.Random Forest Feature Importance With SHAP.Built-in Scikit-learn Method With A Random Feature.Built-in Feature Importance With Scikit-Learn.Global Vs Local Feature Importance Methods.High Cardinality Bias In Random Forests.X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42) From sklearn.model_selection import train_test_split