Recursive Feature Elimination (RFE) for Feature Selection in Python
Last Updated on August 28, 2020
Recursive Feature Elimination, or RFE for short, is a popular feature selection algorithm.
RFE is popular because it is easy to configure and use and because it is effective at selecting those features (columns) in a training dataset that are more or most relevant in predicting the target variable.
There are two important configuration options when using RFE: the choice in the number of features to select and the choice of the algorithm used to help choose features. Both of these hyperparameters can be explored, although the performance of the method is not strongly dependent on these hyperparameters being configured well.
In this tutorial, you will discover how to use Recursive Feature Elimination (RFE) for feature selection in Python.
After completing this tutorial, you will know:
- RFE is an efficient approach for eliminating features from a training dataset for feature selection.
- How to use RFE for feature selection for classification and regression predictive modeling problems.
- How to explore the number of selected features and wrapped algorithm used by the RFE procedure.
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