hinge_loss¶
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pai4sk.sml_metrics.hinge_loss(data, pred_decision)¶ Distributed average hinge loss metric. It supports both local and distributed(MPI) implementation.
It supports only binary classification. If the true labels are encoded with +1 and -1, then the hinge loss of a sample is computed as 1 - true_label * predicted_decision. The predicted_decision is the output of the decision_function predict function (the distance of the samples in data to the separating hyperplane). The average hinge loss is the average of (1 - true_label * predicted_decision) across samples. The metric is implemented in a distributed manner for MPI execution.
- Parameters
data (Supports the following input data-types :) –
Dense matrix (ndarray) of correct labels.
SnapML data partition. This includes the correct labels.
pred_decision (Predicted values of the decision function.) – array-like, shape = (n_samples,)
- Returns
hinge_loss_value – Returns the average hinge loss of the samples in data.
- Return type