What is feature selection?
Feature Selection is a method used to select the relevant features for the model to train on. We need feature selection to remove the irrelevant features which leads the model to under-perform.
Feature selection important for linear models. It can make model performance better through selecting the most importance features and remove irrelevant features in order to make a prediction and it can also avoid overfitting, underfitting and bias-variance tradeoff.
Here are some of the feature selections:
- Principal Component Analysis
- Neighborhood Component Analysis
- ReliefF Algorithm