confusion_matrix (original) (raw)
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 \(C\) is such that \(C_{i, j}\)is equal to the number of observations known to be in group \(i\) and predicted to be in group \(j\).
Thus in binary classification, the count of true negatives is\(C_{0,0}\), false negatives is \(C_{1,0}\), true positives is\(C_{1,1}\) and false positives is \(C_{0,1}\).
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 in y_true or y_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.
References
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))