How to do Feature Selection – recursive feature elimination in R

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How to do Feature Selection – recursive feature elimination in R

Recursive feature elimination (RFE) is a feature selection technique that recursively removes the least important features from the dataset. The goal of RFE is to select a subset of features that are most informative and relevant to the target variable, while reducing the dimensionality of the data.

RFE works by training a model on the entire dataset, then it removes the least important feature and retrains the model. This process is repeated until the desired number of features is reached. At each iteration, the feature importance is calculated and the feature with the lowest importance is removed.

In R, the caret package provides the RFE() function that can be used for recursive feature elimination. The function takes the model and dataset as input and returns the feature importance. The function also takes in the number of features to be selected, and the method for measuring feature importance.

Another option is to use the Boruta package that performs RFE by using random forest. It uses permutation importance for feature selection and returns a list of features ranked by importance.

In summary, Recursive feature elimination (RFE) is a feature selection technique that recursively removes the least important features from the dataset. The goal of RFE is to select a subset of features that are most informative and relevant to the target variable, while reducing the dimensionality of the data. RFE works by training a model on the entire dataset, then it removes the least important feature and retrains the model. This process is repeated until the desired number of features is reached. In R, the caret package provides the RFE() function and Boruta package that can be used for recursive feature elimination and return a list of features ranked by importance.

 

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How to do Feature Selection – recursive feature elimination in R

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