confusion_matrix#
- sklearn.metrics.confusion_matrix(y_true, y_pred, *, labels=None, sample_weight=None, normalize=None)[source]#
Compute confusion matrix to evaluate the accuracy of a classification.
By definition a confusion matrix
is such that is equal to the number of observations known to be in group and predicted to be in group .Thus in binary classification, the count of true negatives is
, false negatives is , true positives is and false positives is .Read more in the User Guide.
- Parameters:
- y_truearray-like of shape (n_samples,)
Ground truth (correct) target values.
- y_predarray-like of shape (n_samples,)
Estimated targets as returned by a classifier.
- labelsarray-like of shape (n_classes), default=None
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 iny_true
ory_pred
are used in sorted order.- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
Added in version 0.18.
- normalize{‘true’, ‘pred’, ‘all’}, 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:
- Cndarray of shape (n_classes, n_classes)
Confusion matrix whose i-th row and j-th column entry indicates the number of samples with true label being i-th class and predicted label being j-th class.
See also
ConfusionMatrixDisplay.from_estimator
Plot the confusion matrix given an estimator, the data, and the label.
ConfusionMatrixDisplay.from_predictions
Plot the confusion matrix given the true and predicted labels.
ConfusionMatrixDisplay
Confusion Matrix visualization.
References
[1]Wikipedia entry for the Confusion matrix (Wikipedia and other references may use a different convention for axes).
Examples
>>> from sklearn.metrics import confusion_matrix >>> y_true = [2, 0, 2, 2, 0, 1] >>> y_pred = [0, 0, 2, 2, 0, 2] >>> confusion_matrix(y_true, y_pred) array([[2, 0, 0], [0, 0, 1], [1, 0, 2]])
>>> y_true = ["cat", "ant", "cat", "cat", "ant", "bird"] >>> y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"] >>> confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"]) array([[2, 0, 0], [0, 0, 1], [1, 0, 2]])
In the binary case, we can extract true positives, etc. as follows:
>>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel() >>> (tn, fp, fn, tp) (np.int64(0), np.int64(2), np.int64(1), np.int64(1))
Gallery examples#
Release Highlights for scikit-learn 1.5
Visualizations with Display Objects
Post-tuning the decision threshold for cost-sensitive learning
Label Propagation digits active learning