Variable selection is the process of choosing a subset of relevant features or variables for use in model construction. This step is crucial as it can help improve the model's performance, reduce overfitting, and enhance interpretability by eliminating unnecessary or redundant predictors. Proper variable selection ensures that the model focuses on the most significant predictors, particularly when multicollinearity is present, which can obscure the relationships between variables.
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