Description: Recursive Feature Elimination (RFE) is a feature selection technique that aims to improve the performance of a machine learning model by iteratively removing the least important features. This process is based on model evaluation, where the model is initially trained with all available features and then the importance of each feature is assessed. Features that contribute the least to the model’s performance are eliminated in each iteration, and the process is repeated until a desired number of features is reached. RFE is particularly useful in situations where a large number of features are available, which can lead to overfitting issues and increased computation time. By reducing the number of features, not only is the model’s interpretability improved, but its accuracy and efficiency can also be enhanced. This technique can be applied to various machine learning algorithms, including logistic regression, support vector machines, and decision trees, making it a versatile tool in the field of data science and machine learning.
