Variable selection is the process of identifying and choosing the most relevant variables to include in a statistical model, aiming to improve model performance and interpretability. This concept is crucial in contexts like binary logistic regression, where the goal is to predict a binary outcome based on various predictors while avoiding overfitting and ensuring the assumptions of the model are met.
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