fairlearn.metrics.false_negative_rate#
- fairlearn.metrics.false_negative_rate(y_true, y_pred, sample_weight=None, pos_label=None)[source]#
Calculate the false negative rate (also called miss rate).
Read more in the User Guide.
- Return type:
- Parameters:
- y_truearray-like
The list of true values
- y_predarray-like
The list of predicted values
- sample_weightarray-like, optional
A list of weights to apply to each sample. By default all samples are weighted equally
- pos_labelscalar, optional
The value to treat as the ‘positive’ label in the samples. If None (the default) then the largest unique value of the y arrays will be used.
- Returns:
- float
The false negative rate for the data
