Forward selection is a stepwise model selection technique that begins with no predictors in the model and adds variables one at a time based on a specific criterion, usually aiming to improve model performance. This method is often used in multiple regression analysis to identify a subset of predictors that significantly contribute to the model's explanatory power while avoiding overfitting. By iteratively including the most relevant predictors, forward selection can help in building a more parsimonious model that balances complexity and predictive accuracy.
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