confusion_matrix#

cuml.metrics.confusion_matrix( y_true, y_pred, labels=None, sample_weight=None, normalize=None, ) → ndarray[source]#

Compute confusion matrix to evaluate the accuracy of a classification.

Parameters:

y_truearray-like (device or host) shape = (n_samples,): Ground truth (correct) target values.
y_predarray-like (device or host) shape = (n_samples,): Estimated target values.
labelsarray-like (device or host) shape = (n_classes,), optional: List of labels to index the matrix. This may be used to reorder or select a subset of labels. If None is given, those that appear at least once in y_true or y_pred are used in sorted order.
sample_weightarray-like (device or host) shape = (n_samples,), optional: Sample weights.
normalizestring in [‘true’, ‘pred’, ‘all’] or None (default=None): Normalizes confusion matrix over the true (rows), predicted (columns) conditions or all the population. If None, confusion matrix will not be normalized.

Returns: