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XGBoost will produce different values for feature importances with different hyperparameters on the same dataset. When using XGBoost as a feature selection algorithm for a different model, should I therefore optimize the hyperparameters first? Or there are no hard and fast rules, and in practice I should try say both the default and the optimized set of hyperparameters and see what really works?

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    $\begingroup$ You shouldnt use xgboost as a feature selection algorithm for a different model. Different models use different features in different ways. Theres no reason to believe features improtant for one will work in the same way for another. $\endgroup$ – Matthew Drury Jul 22 '17 at 3:25
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    $\begingroup$ @MatthewDrury I'll write this up as an answer, but if you'd prefer to make this comment into an answer, I'll delete my quotation. $\endgroup$ – Sycorax Jul 3 '18 at 15:21
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From comments, Matthew Drury writes:

You shouldn't use xgboost as a feature selection algorithm for a different model. Different models use different features in different ways. Theres no reason to believe features important for one will work in the same way for another.

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