Feature Selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. It is one of the most important steps in machine learning, particularly in supervised learning, with a high number of features.
Some popular techniques for feature selection are:
1. Filter Methods 2. Wrapper Methods 3. Embedded Methods
Filter methods involve rank-based feature selection, in which the features are ranked by their relevance to the target variable. Some of the popular filters methods include Pearson’s Correlation Coefficient, Mutual Information Gain.
Wrapper Methods determine which features contribute the most to the model’s accuracy. Example of a Wrapper Method is the Recursive Feature Elimination (RFE) algorithm.
Embedded Methods learn which features contribute the most to the objective while fitting the model. Example of an Embedded Method is the Lasso Regression model.
Why is Feature Selection important? Feature selection helps to reduce dimensionality, allowing the model to become more interpretable, understandable and reducing the likelihood of overfitting. By reducing dimensionality, we avoid the curse of dimensionality where a higher number of features reduce the model’s efficiency, given that these features are irrelevant. Additionally, reducing the number of features helps in reducing the computational cost associated with model training and selection.
Feature Selection can improve the model’s accuracy, decrease the risk of overfitting and improves computational efficiency.