Forward Regression In R. Forward Selection chooses a subset of the predictor variables for the final model. We can do forward stepwise in context of linear regression whether n is less than p or n is greater than p.
Forward Selection chooses a subset of the predictor variables for the final model. That input dataset needs to have a target variable and at least one predictor variable. StepAIC is an automated method that returns back the optimal set of features.
Apply step to these models to perform forward stepwise regression.
Forward selection is a very attractive approach because its both tractable and it gives a good sequence of models. Running a regression model with many variables including irrelevant ones will lead to a needlessly complex model. Set the first argument to null_model and set direction forward. For backward variable selection I used the following command.
