ConfusionMatrix

ConfusionMatrix(classes=None) Confusion Matrix for binary-class and multi-class classification.

Attributes

• classes

• data

• last_y_pred

• last_y_true

• majority_class

• n_classes

• n_samples

• sample_correction

• shape

• sum_col

• sum_diag

• sum_row

• total_weight

• weight_majority_classifier

• weight_no_change_classifier

Examples

>>> from river import metrics

>>> y_true = ['cat', 'ant', 'cat', 'cat', 'ant', 'bird']
>>> y_pred = ['ant', 'ant', 'cat', 'cat', 'ant', 'cat']

>>> cm = metrics.ConfusionMatrix()

>>> for yt, yp in zip(y_true, y_pred):
...     cm = cm.update(yt, yp)

>>> cm
       ant  bird   cat
 ant     2     0     0
bird     0     0     1
 cat     1     0     2

>>> cm['bird']['cat']
1.0

Methods

false_negatives

Parameters

• label

false_positives

Parameters

• label

reset

revert

Parameters

• y_true
• y_pred
• sample_weight
• correction

true_negatives

Parameters

• label

true_positives

Parameters

• label

update

Parameters

• y_true
• y_pred
• sample_weight

Notes

This confusion matrix is a 2D matrix of shape `(n_classes, n_classes)`, corresponding
to a single-target (binary and multi-class) classification task.

Each row represents `true` (actual) class-labels, while each column corresponds
to the `predicted` class-labels. For example, an entry in position `[1, 2]` means
that the true class-label is 1, and the predicted class-label is 2 (incorrect prediction).

This structure is used to keep updated statistics about a single-output classifier's
performance and to compute multiple evaluation metrics.