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Forward selection is a method used in statistical modeling and machine learning for selecting a subset of predictors to use in a model. This approach starts with an empty model with no predictors and systematically adds variables one by one based on specific criteria, typically aiming to improve the model performance or accuracy.

As each variable is added, the model is evaluated to determine if the addition leads to a significant improvement in fit, often guided by metrics such as p-values, R-squared changes, or information criteria like AIC or BIC. The process continues until adding more variables does not enhance the model significantly, thereby resulting in a final model that contains only those predictors deemed beneficial.

This method contrasts with approaches like backward elimination, which starts with all available variables and systematically removes those that do not contribute significantly to the model, or exhaustive search methods that evaluate all combinations of variables. Random selection approaches would not follow the systematic criteria-based method of forward selection either, which ensures that only the most relevant predictors are included in the final model.