Backward elimination is a feature selection technique used in statistical modeling and machine learning, where models start with all potential features and iteratively remove the least significant ones based on specific criteria. This method helps in identifying a simpler model that maintains predictive accuracy while reducing overfitting and improving interpretability. It balances the trade-off between complexity and performance by allowing only the most impactful features to remain in the model.
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