Multi-target prediction (multitarget)

Multi-target prediction tries to achieve better prediction accuracy or speed through prediction of multiple dependent variables at once. It works on multi-target data, which is also supported by Orange’s tab file format using multiclass directive.

List of supported learners:

For evaluation of multi-target methods, see the corresponding section in Multi-target Scoring (scoring).

The addon also includes three sample datasets:

  • - dataset with 5 multi-class class variables
  • - dataset with 3 multi-class class variables
  • - dataset with 6 binary class variables (a multi-label dataset)

Example of loading an included dataset:

import Orange
data ='')


The following example uses a simple multi-target data set (generated with to show some basic functionalities (part of

import Orange
data ='')
print 'Features:', data.domain.features
print 'Classes:', data.domain.class_vars
print 'First instance:', data[0]
print 'Actual classes:', data[0].get_classes()

Multi-target learners can build prediction models (classifiers) which then predict (multiple) class values for a new instance (continuation of

majority = Orange.classification.majority.MajorityLearner()
mt_majority = Orange.multitarget.binary.BinaryRelevanceLearner(learner = majority)
c_majority = mt_majority(data)
print 'Majority predictions:\n', c_majority(data[0])

mt_majority = Orange.multitarget.chain.ClassifierChainLearner(learner = majority)
c_majority = mt_majority(data)
print 'Chain Majority predictions:\n', c_majority(data[0])

pls = Orange.multitarget.pls.PLSClassificationLearner()
c_pls = pls(data)
print 'PLS predictions:\n', c_pls(data[0])

clust_tree = Orange.multitarget.tree.ClusteringTreeLearner()
c_clust_tree = clust_tree(data)
print 'Clustering Tree predictions: \n', c_clust_tree(data[0])

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