Why Am I Getting an 'UndefinedMetricWarning' When Calculating F-Score in Scikit-learn?

Troubleshooting "UndefinedMetricWarning" in F-Score Calculation

Sklearn's F-score metric can sometimes trigger an "UndefinedMetricWarning" when there are labels in the ground truth data (y_test) that were not predicted (y_pred). This occurs because the F-score is undefined for labels with no predicted samples. In such cases, the score is set to 0.0.

In your case, you may have noticed that the error appears only during the first execution and not subsequently. This is because warnings are only displayed once by default. You can modify this behavior by setting the warnings.filterwarnings() function to 'always' to display warnings every time.

To avoid the warning, you have two options:

1. Ignore labels without predictions: Specify the labels you are interested in by setting the labels parameter to the unique values in y_pred. This will exclude labels with no predicted samples and the warning will disappear:

import numpy as np

metrics.f1_score(y_test, y_pred, average='weighted', labels=np.unique(y_pred))

2. Handle undefined metrics explicitly: If you want to handle undefined metrics in a custom way, you can use the error_score parameter to assign a specific value (e.g., -1, 0, or NaN) to undefined metrics.

Regarding the trailing "precision', 'predicted', average, warn_for)" error message, it is a bug in scikit-learn 0.18.1 that has been fixed in later versions. The error message should not affect your results.

The above is the detailed content of Why Am I Getting an 'UndefinedMetricWarning' When Calculating F-Score in Scikit-learn?. For more information, please follow other related articles on the PHP Chinese website!