Embedded feature selection

1. What is embedded feature selection?
2. What are the three types of feature selection methods?
3. What is embedded method in ML?

What is embedded feature selection?

In an embedded method, feature selection is integrated or built into the classifier algorithm. During the training step, the classifier adjusts its internal parameters and determines the appropriate weights/importance given for each feature to produce the best classification accuracy.

What are the three types of feature selection methods?

Overview. There are three types of feature selection: Wrapper methods (forward, backward, and stepwise selection), Filter methods (ANOVA, Pearson correlation, variance thresholding), and Embedded methods (Lasso, Ridge, Decision Tree).

What is embedded method in ML?

Embedded methods combine the qualities' of filter and wrapper methods. It's implemented by algorithms that have their own built-in feature selection methods. Some of the most popular examples of these methods are LASSO and RIDGE regression which have inbuilt penalization functions to reduce overfitting.