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
Default →
2
The number of decimals to display in each cell.
-
cm
Default →
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.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
Indicate if the current metric is better than another one.
Parameters
- other
revert
Revert the metric.
Parameters
- y_true
- y_pred
- w — defaults to
1.0
update
Update the metric.
Parameters
- y_true
- y_pred
- w — defaults to
1.0
works_with
Indicates whether or not a metric can work with a given model.
Parameters
- model — 'base.Estimator'