MultiLabelConfusionMatrix¶
MultiLabelConfusionMatrix(labels=None) Multi-label Confusion Matrix.
Attributes¶
-
data
-
exact_match_cnt
-
jaccard_sum
-
labels
-
last_y_pred
-
last_y_true
-
n_labels
-
n_samples
-
precision_sum
-
recall_sum
-
sample_correction
-
shape
Methods¶
reset
revert
Parameters
- y_true
- y_pred
- sample_weight
- correction
update
Parameters
- y_true
- y_pred
- sample_weight
Notes¶
This confusion matrix corresponds to a 3D matrix of shape `(n_labels, 2, 2)` meaning
that each `label` has a corresponding binary `(2x2)` confusion matrix.
The first dimension corresponds to the `label`, the second and third dimensions
are binary indicators for the `true` (actual) vs `predicted` values. For example,
an entry in position `[2, 0, 1]` represents a miss-classification of label 2.
This structure is used to keep updated statistics about a multi-output classifier's
performance and to compute multiple evaluation metrics.