Is there a standard method for choosing features from different feature selection techniques?

I have four different feature selection techniques, Backwards Elimination, Lasso, feature_importances, and Recursive feature selection. Each technique returns slightly different results. For example,

  • Backwards Elimination: Spread Direction
  • Lasso: Spread Move, and Spread
  • Feature_Importances_: Spread Percentage and Spread Money
  • Recursive: Spread Money

is there a standard method when choosing features from different models? Should you choose the features that each model returns or is there a preferred method when doing this?

Topic feature-engineering machine-learning

Category Data Science

1
answered at

There is no standard way. It depends on the goal of feature selection.

One useful method is cross-validation. If the evaluation metric score improves during cross-validation, then removing the feature improves the predictive ability of the model.

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