What is feature selection?
Feature selection is used to determine the essential features in the dataset. It is extremely useful when the dataset includes too many variables, many of which are often irrelevant. Feature selection allows us to reduce the time and computing power needed for model training without losing performance.
Why is it important?
Feature selection reduces the time, space, and computing power required for model training by eliminating irrelevant features. It can also increase the accuracy and interpretability of the model by including only the most informative variables, thus removing redundant noise. Finally, feature selection allows diminishing overfitting by showing the model too many variables it can simply recognize.