mars.learn.metrics.multilabel_confusion_matrix¶
- mars.learn.metrics.multilabel_confusion_matrix(y_true, y_pred, *, sample_weight=None, labels=None, samplewise=False, session=None, run_kwargs=None)[源代码]¶
Compute a confusion matrix for each class or sample.
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.
- 参数
y_true ({array-like, sparse matrix} of shape (n_samples, n_outputs) or (n_samples,)) – Ground truth (correct) target values.
y_pred ({array-like, sparse matrix} of shape (n_samples, n_outputs) or (n_samples,)) – Estimated targets as returned by a classifier.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.
labels (array-like of shape (n_classes,), default=None) – A list of classes or column indices to select some (or to force inclusion of classes absent from the data).
samplewise (bool, default=False) – In the multilabel case, this calculates a confusion matrix per sample.
- 返回
multi_confusion – 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 inlabels
, otherwise the results will be returned in sorted order by default.- 返回类型
ndarray of shape (n_outputs, 2, 2)
参见
confusion_matrix
Compute confusion matrix to evaluate the accuracy of a classifier.
提示
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.实际案例
Multiclass case:
>>> import mars.tensor as mt >>> from mars.learn.metrics import multilabel_confusion_matrix >>> 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]], [[5, 0], [1, 0]], [[2, 1], [1, 2]]])
Multilabel-indicator case not implemented yet.