sklearn.metrics.multilabel_confusion_matrix

sklearn.metrics.multilabel_confusion_matrix(y_true, y_pred, sample_weight=None, labels=None, samplewise=False)[source]

Compute a confusion matrix for each class or sample

New in version 0.21.

Compute class-wise (default) or sample-wise (samplewise=True) multilabel confusion matrix to evaluate the accuracy of a classification, and output confusion matrices for each class or sample.

In multilabel confusion matrix \(MCM\), the count of true negatives is \(MCM_{:,0,0}\), false negatives is \(MCM_{:,1,0}\), true positives is \(MCM_{:,1,1}\) and false positives is \(MCM_{:,0,1}\).

Multiclass data will be treated as if binarized under a one-vs-rest transformation. Returned confusion matrices will be in the order of sorted unique labels in the union of (y_true, y_pred).

Read more in the User Guide.

Parameters
y_true1d array-like, or label indicator array / sparse matrix

of shape (n_samples, n_outputs) or (n_samples,) Ground truth (correct) target values.

y_pred1d array-like, or label indicator array / sparse matrix

of shape (n_samples, n_outputs) or (n_samples,) Estimated targets as returned by a classifier

sample_weightarray-like of shape = (n_samples,), optional

Sample weights

labelsarray-like

A list of classes or column indices to select some (or to force inclusion of classes absent from the data)

samplewisebool, default=False

In the multilabel case, this calculates a confusion matrix per sample

Returns
multi_confusionarray, shape (n_outputs, 2, 2)

A 2x2 confusion matrix corresponding to each output in the input. When calculating class-wise multi_confusion (default), then n_outputs = n_labels; when calculating sample-wise multi_confusion (samplewise=True), n_outputs = n_samples. If labels is defined, the results will be returned in the order specified in labels, otherwise the results will be returned in sorted order by default.

See also

confusion_matrix

Notes

The multilabel_confusion_matrix calculates class-wise or sample-wise multilabel confusion matrices, and in multiclass tasks, labels are binarized under a one-vs-rest way; while confusion_matrix calculates one confusion matrix for confusion between every two classes.

Examples

Multilabel-indicator case:

>>> import numpy as np
>>> from sklearn.metrics import multilabel_confusion_matrix
>>> y_true = np.array([[1, 0, 1],
...                    [0, 1, 0]])
>>> y_pred = np.array([[1, 0, 0],
...                    [0, 1, 1]])
>>> multilabel_confusion_matrix(y_true, y_pred)
array([[[1, 0],
        [0, 1]],
<BLANKLINE>
       [[1, 0],
        [0, 1]],
<BLANKLINE>
       [[0, 1],
        [1, 0]]])

Multiclass case:

>>> y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
>>> y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
>>> multilabel_confusion_matrix(y_true, y_pred,
...                             labels=["ant", "bird", "cat"])
array([[[3, 1],
        [0, 2]],
<BLANKLINE>
       [[5, 0],
        [1, 0]],
<BLANKLINE>
       [[2, 1],
        [1, 2]]])