Feature-Selection

Forward selection is an iterative method in which we start with having no feature in the model. In each iteration, we keep adding the feature that best improves our model till the addition of a new variable does not improve the performance of the model.

Backward elimination is a feature selection technique while building a machine learning model. It is used to remove those features that do not have a significant effect on the dependent variable or prediction of output.