Recursive feature elimination (RFE) is a feature selection method that recursively removes the least important features from a dataset to improve the performance of a machine learning model. This technique helps in identifying the most relevant features, which can enhance model accuracy and reduce overfitting, especially in complex datasets like terahertz data.
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