Backward elimination is a model selection technique used in statistical modeling to refine the selection of predictor variables. It starts with a full model that includes all potential predictors and iteratively removes the least significant variables based on their p-values, until only statistically significant predictors remain. This method helps in improving model performance by reducing overfitting and enhancing interpretability while ensuring that the model retains its predictive power.
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