In the Filter method, features are selected based on statistical measures. It is independent of the learning algorithm and requires less computational time. Information gain, chi-square test, Fisher score, correlation coefficient, and variance threshold are some of the statistical measures used to understand the importance of the features.
The Filter methodology uses the selected metric to identify irrelevant attributes and also filter out redundant columns from the models. It gives the option of isolating selected measures that enrich a model. The columns are ranked following the calculation of the feature scores.