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Currently, the positive outcome is just whichever is the first row in the test data, which is random. This throws off performance metrics! We need to ensure the user's preferred positive class is preserved. We should probably add an argument for this to specify the "positive" label in case the outcome isn't a factor? This issue only applies to binary classification.
The text was updated successfully, but these errors were encountered:
Currently, the positive outcome is just whichever is the first row in the test data, which is random. This throws off performance metrics! We need to ensure the user's preferred positive class is preserved. We should probably add an argument for this to specify the "positive" label in case the outcome isn't a factor? This issue only applies to binary classification.
The text was updated successfully, but these errors were encountered: