Recursive-Feature-Elimination-RFE-by-Using-Tree-Based-and-Gradient-Boosting-Algorithm

Recursive Feature Elimination (RFE) by Using Tree Based and Gradient Boosting Algorithm

Download Working File: https://github.com/laxmimerit/Recursive-Feature-Elimination-RFE-by-Using-Tree-Based-and-Gradient-Boosting-Algorithm

Recursive Feature Elimination (RFE) by Using Tree-Based and Gradient-Based Estimators Recursive Feature Elimination (RFE) as its title suggests recursively removes features, builds a model using the remaining attributes and calculates model accuracy. RFE is able to work out the combination of attributes that contribute to the prediction on the target variable (or class). Scikit Learn does most of the heavy lifting just import RFE from sklearn.feature_selection and pass any classifier model to the RFE() method with the number of features to select. Using familiar Scikit Learn syntax, the .fit() method must then be called.

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