Permutation Feature Importance | Machine Learning Interpretability
Permutation feature importance is a model agnostic interpretability method that can be used to interpret both explainable and black-box machine learning models. In this video, we explain how permutation feature importance works. Through an illustration, you'll learn the step-by-step process of evaluating feature importance by shuffling the values of a feature and measuring performance drops. Want to learn more? Check out our course: https://www.trainindata.com/p/machine...
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