Construct · 1 fields · 1 citations

permutation feature importance: a standalone construct

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Machine learning

the drop in a model's accuracy when one feature's values are randomly shuffled, used as a measure of that feature's importance.

In machine learning, permutation feature importance refers to the drop in a model's accuracy when one feature's values are randomly shuffled, used as a measure of that feature's importance. This meaning comes from Breiman (2001) in Machine Learning.[1]

Unit
Recovery means
Measured by

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Nearby constructs

accuracy3 fields · 6 citationsjingle · one word, three constructs feature2 fields · 4 citationsjingle · one word, two constructs learning6 fields · 14 citationsjingle · one name, six constructs

References

Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/a:1010933404324

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