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:

float

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