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if a feature appears as the most important among a variety of models, does that make this feature empirically important?
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Lets say that I train a variety of different models - SVM, XGBoost, LogisticRegression, Random Forest, KNeighbors, etc. I then take the permutation importance of each model, and find that Column12
is the most important feature among all the models.
Assuming that this is not a mistake (i.e. hyperparams were tuned and retrained, cross-validation was used, strongly correlated columns were eliminated, etc...), does that mean that Column12
has explicit predictive power as a feature? Why or why not?
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