open3d.ml.tf.models.confusion_matrix

open3d.ml.tf.models.confusion_matrix(y_true, y_pred, *, labels=None, sample_weight=None, normalize=None)

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_true (array-like of shape (n_samples,)) – Ground truth (correct) target values.

• y_pred (array-like of shape (n_samples,)) – Estimated targets as returned by a classifier.

• labels (array-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_weight (array-like of shape (n_samples,), default=None) –

  Sample weights.

  New 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

C – Confusion matrix whose i-th row and j-th column entry indicates the number of samples with true label being i-th class and prediced label being j-th class.

Return type

ndarray of shape (n_classes, n_classes)

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)
(0, 2, 1, 1)