Why Does My F-Score Throw a 'UndefinedMetricWarning' in Python and How Do I Fix It?

F-Score Warning: Undefined Metrics and Missing Predicted Samples

In the context of classification tasks, the F-score metric is commonly used to evaluate model performance. However, when encountering the "UndefinedMetricWarning: F-score is ill-defined," error, it indicates that the F-score cannot be calculated for certain labels due to no predicted samples.

This issue arises when a label present in the true label set (y_test) does not appear in the predicted label set (y_pred). Consequently, calculating the F-score for such labels results in an undefined value. To handle this situation, scikit-learn assigns a value of 0.0 to the F-score of these labels.

One way to observe this scenario is through an example. Consider a situation where label '2' is present in y_test but absent in y_pred:

>>> set(y_test) - set(y_pred)
{2}

Since there is no predicted sample for label '2,' the F-score for this label is considered 0.0. As the calculation includes a score of 0, a warning is displayed by scikit-learn to alert about the undefined metric.

This warning is raised only the first time it occurs. This behavior is due to the default setting of warnings in Python, which ensures that specific warnings are shown only once.

To suppress this warning, you can disable it using warnings.filterwarnings('ignore'):

import warnings
warnings.filterwarnings('ignore')

Alternatively, you can explicitly specify the labels of interest, excluding those without predicted samples:

>>> metrics.f1_score(y_test, y_pred, average='weighted', labels=np.unique(y_pred))
0.91076923076923078

By specifying the labels that were actually predicted, the warning can be avoided.

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