Feature selection methods

1. What are feature selection methods?
2. What are the three types of feature selection methods?
3. How many ways a feature can be selected?
4. What are filter methods in feature selection?

What are feature selection methods?

Feature Selection is the method of reducing the input variable to your model by using only relevant data and getting rid of noise in data. It is the process of automatically choosing relevant features for your machine learning model based on the type of problem you are trying to solve.

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).

How many ways a feature can be selected?

Let's build a simple voting selector that ensembles three different features selection methods: 1 A filter method based on Pearson correlation. 2 An unsupervised method based on multicollinearity. 3 A wrapper, Recursive Feature Elimination.

What are filter methods in feature selection?

Filter methods measure the relevance of features by their correlation with dependent variable while wrapper methods measure the usefulness of a subset of feature by actually training a model on it. Filter methods are much faster compared to wrapper methods as they do not involve training the models.