To enable the algorithms to train faster, and to reduce the complexity and overfitting of the model, in addition to improving its accuracy, you can use many feature selection algorithms and techniques. We are going to look at three different feature selection methods: filter methods, wrapper methods, and embedded methods. Let's discuss the various methodologies and techniques.
Feature selection algorithms
Filter methods
In filter methods, each feature will be assigned a score, computed by different statistical measures. In other words, these methods rank features by considering the relationships between the features and the targets. Filter methods are usually used in the pre-processing phase:
