What Are the Common Techniques for Feature Selection?
There are several techniques for feature selection, each with its own advantages and limitations. Some of the most commonly used methods include:
Filter Methods: These techniques use statistical measures to score the relevance of features. Examples include correlation coefficient scores, chi-square tests, and mutual information. Wrapper Methods: These methods evaluate the performance of a subset of features by running a specific machine learning model. Techniques like recursive feature elimination (RFE) fall into this category. Embedded Methods: These techniques perform feature selection during the model training process. Regularization methods like LASSO (Least Absolute Shrinkage and Selection Operator) and Ridge Regression are commonly used embedded methods.
