Feature Selection helps reduce the complexity of a machine learning model by removing irrelevant or redundant data, thus preventing overfitting and improving accuracy. It works by evaluating each feature based on its predictive power and significance, then retaining only the ones that contribute meaningfully to the model’s predictions. This process can be done using techniques like correlation analysis, decision trees, or algorithms like Recursive Feature Elimination (RFE).
Feature Selection Example
For example, in a dataset predicting house prices, Feature Selection might remove irrelevant features like the house’s paint color, focusing instead on variables like location, size, and the number of bedrooms, which have a direct impact on price.