- Permutation Importance
Insignificant feature can be shuffled and won't make a big impact on the model
Permutation importance is great because it created simple numeric measures to see which features mattered to a model. This helped us make comparisons between features easily, and you can present the resulting graphs to non-technical audiences.
But it doesn't tell you how each features matter. If a feature has medium permutation importance, that could mean it has
- a large effect for a few predictions, but no effect in general, or
- a medium effect for all predictions.
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Partial Plots and GAM
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SHAP VALUES
To understand individual predictions
Credits to https://www.kaggle.com/dansbecker/advanced-uses-of-shap-values. Thanks for the great tutorial
How:
Change the feature values of an instance before making the predictions and we analyze how the prediction changes
Criterion for counterfactual explanations:
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the minimum changes to the features so that the prediction changes
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Change as few feature as possible
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the change should be achieveable e.g you cannot enlarge the size of your house, even model shows 10 m2 bigger could raise the rent