deephyper.ensemble.loss.ZeroOneLoss#
- class deephyper.ensemble.loss.ZeroOneLoss(predict_proba: bool = False)[source]#
Bases:
Loss
Zero-One loss for classification (a.k.a, error rate).
It has value
1
if the prediction is wrong and0
if it is correct.- Parameters:
predict_proba (bool, optional) – A boolean indicating if
y_pred
contains predicted categorical probabilities. Defaults toFalse
for label predictions.
Methods
- __call__(y_true: ndarray, y_pred: ndarray | Dict[str, ndarray]) ndarray [source]#
Compute the zero-one loss.
- Parameters:
y_true (np.ndarray) – the true target values. It should be an array of integers representing the true class labels.
y_pred (np.ndarray or Dict[str, np.ndarray]) – the predicted target values. If it is a _Dict[str, np.ndarray]_ then it should contain a key
"loc"
.
- Returns:
the loss value with first dimension
n_samples
.- Return type:
np.ndarray