This is documentation for Orange 2.7. For the latest documentation, see Orange 3.

# Calibration Plot¶

Shows the match between the classifiers’ probability predictions and actual class probabilities.

## Signals¶

- Inputs:
- Evaluation Results (orngTest.ExperimentResults)
Results of classifiers’ tests on data

- Outputs:
- None

## Description¶

Calibration plot plots the class probabilities against those predicted by the classifier(s).

Option `Target class` chooses the positive class. In case there are more
than two classes, the widget considers all other classes as a single, negative
class. If the test results contain more than one classifier, the user can
choose which curves she or he wants to see plotted.

The diagonal represents the optimal behaviour; the close the classifier gets, the more accurate its predictions.

If `Show rug` is enable, ticks at the bottom and the top of the graph
represents negative and positive examples (respectively). Their position
corresponds to classifier’s probability prediction and the color shows the
classifier. On the bottom of the graph, the points to the left are those
which are (correctly) assigned a low probability of the target class, and
those to the right are incorrectly assigned high probabilities. On the top
of the graph, the instances to the right are correctly assigned hight
probabilities and vice versa.

## Example¶

At the moment, the only widget which give the right type of the signal
needed by the Calibration Plot is *Test Learners*. The Calibration Plot
will hence always follow Test Learners and, since it has no outputs, no other
widgets follow it. Here is a typical example.