# ClassificationReport¶

A report for monitoring a classifier.

This class maintains a set of metrics and updates each of them every time update is called. You can print this class at any time during a model's lifetime to get a tabular visualization of various metrics.

You can wrap a metrics.ClassificationReport with utils.Rolling in order to obtain a classification report over a window of observations. You can also wrap it with utils.TimeRolling to obtain a report over a period of time.

## Parameters¶

• decimals – defaults to 2

The number of decimals to display in each cell.

• cm – defaults to None

This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.

## Attributes¶

• bigger_is_better

Indicate if a high value is better than a low one or not.

• requires_labels

Indicates if labels are required, rather than probabilities.

• works_with_weights

Indicate whether the model takes into consideration the effect of sample weights

## Examples¶

>>> from river import metrics

>>> y_true = ['pear', 'apple', 'banana', 'banana', 'banana']
>>> y_pred = ['apple', 'pear', 'banana', 'banana', 'apple']

>>> report = metrics.ClassificationReport()

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

>>> print(report)
Precision   Recall   F1       Support
<BLANKLINE>
apple       0.00%    0.00%    0.00%         1
banana     100.00%   66.67%   80.00%         3
pear       0.00%    0.00%    0.00%         1
<BLANKLINE>
Macro      33.33%   22.22%   26.67%
Micro      40.00%   40.00%   40.00%
Weighted      60.00%   40.00%   48.00%
<BLANKLINE>
40.00% accuracy


## Methods¶

get

Return the current value of the metric.

is_better_than
revert

Revert the metric.

Parameters

• y_true
• y_pred
• sample_weight – defaults to 1.0
update

Update the metric.

Parameters

• y_true
• y_pred
• sample_weight – defaults to 1.0
works_with

Indicates whether or not a metric can work with a given model.

Parameters

• model (river.base.estimator.Estimator)