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

# Mean (`mean`)ΒΆ

Accuracy of a regressor is often compared with the accuracy achieved
by always predicting the average value. The “learning algorithm”
computes the average and represents it with a regressor of type
`Orange.classification.ConstantClassifier`.

Examples

The following example compares the mean squared error of always predicting the average with the error of a tree learner.

```
import Orange
housing = Orange.data.Table("housing")
treeLearner = Orange.classification.tree.TreeLearner() #Orange.regression.TreeLearner()
meanLearner = Orange.regression.mean.MeanLearner()
learners = [treeLearner, meanLearner]
res = Orange.evaluation.testing.cross_validation(learners, housing)
MSEs = Orange.evaluation.scoring.MSE(res)
print "Tree: %5.3f" % MSEs[0]
print "Default: %5.3f" % MSEs[1]
```