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Feature selection is widely used in nearly all data science pipelines. Hence I have created functions that do a form of backward stepwise selection based on the XGBoost classifier feature importance and a set of other input values with the goal to return the number of features to keep in regard to a prefered AUC-score.

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pipeline automation interpretability machine-learning python functions-python xgboost feature-engineering feature-selection feature-importance pipelines-supervised-learning machine-learning-pipelines

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