Variable selection is the process of identifying and choosing the most relevant variables for inclusion in a statistical model. This step is crucial in improving the model's performance, interpretability, and generalizability, particularly in logistic regression for binary outcomes where the focus is on predicting the probability of a specific event occurring. Proper variable selection can help reduce overfitting and enhance the clarity of relationships between predictors and the response variable.
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