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What is the process of assessing a classifier's performance using scikit-learn's classification_report when the test data does not contain a particular class?

asked 2021-12-17 11:00:00 +0000

devzero gravatar image

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answered 2022-05-08 09:00:00 +0000

david gravatar image

When the test data does not contain a particular class, scikit-learn's classification_report will throw a warning stating that there are no samples of that class in the true labels. In such a case, the classifier's performance metrics, including accuracy, precision, recall, and F1-score, will not be reported for that particular class.

It is important to note that if a classifier's performance is evaluated on an imbalanced dataset, where one class has significantly fewer samples than the rest, the absence of that class in the test data could significantly affect the classifier's performance metrics. It is, therefore, crucial to carefully select a representative test set while evaluating a classifier's performance.

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Asked: 2021-12-17 11:00:00 +0000

Seen: 10 times

Last updated: May 08 '22