Why do we learn feature importance that we've learned?

In the ungraded lab C2_W2_Lab_3_Feature_Selection, feature selection methods use statistical tests or greedy approaches to find a subset of features based on a scoring strategy on an estimator. In this approach, data is not modified when fed into the estimator.

Permutation importance shows how sensitive an estimator is to the exact values (as described here) to understand feature importances. In this approach, data is modified when fed into the estimator. To learn more, do visit the scikit learn link provided in the linked topic.

As far a different feature selection techniques is concerned, the methods to use depend on the size / type of the underlying dataset (eg: permutation importance is appliable only on tabular dataset) and eventually the performance on validation set.

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