Introduced on day 17
Permutation importance
The drop in a model's score on held-out data when one column's values are randomly shuffled; a measure of how much this model relies on that column.
In detail
Model-agnostic and computed with the fitted model held fixed. It understates correlated features because the model can use the copy, so permute correlated blocks together or cluster them first. It is not the effect of changing the feature in the world, and it is only informative if the model predicts well.