Feature importance refers to a technique used to assign a score to each input feature based on how useful it is for predicting the target variable. This concept helps in identifying which features contribute the most to the model's predictive capability, guiding decisions on dimensionality reduction and feature selection. Understanding feature importance can significantly enhance model interpretability, allowing for better insights and performance optimization.
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