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src/o/r/Orange-2.7.2/docs/tutorial/rst/code/regression-tree.py   Orange(Download)
import Orange
 
data = Orange.data.Table("housing.tab")
tree = Orange.regression.tree.TreeLearner(data, m_pruning=2., min_instances=20)
print tree.to_string()

src/o/r/Orange-2.7.2/docs/tutorial/rst/code/classification-other.py   Orange(Download)
train = Orange.data.Table([d for d in data if d not in test])
 
tree = Orange.regression.tree.TreeLearner(train, same_majority_pruning=1, m_pruning=2)
tree.name = "tree"
knn = Orange.classification.knn.kNNLearner(train, k=21)

src/o/r/Orange-2.7.2/docs/tutorial/rst/code/regression-cv.py   Orange(Download)
rf = Orange.ensemble.forest.RandomForestLearner()
rf.name = "rf"
tree = Orange.regression.tree.TreeLearner(m_pruning = 2)
tree.name = "tree"
 

src/o/r/Orange-2.7.2/docs/tutorial/rst/code/regression-other.py   Orange(Download)
rf = Orange.ensemble.forest.RandomForestLearner(train)
rf.name = "rf"
tree = Orange.regression.tree.TreeLearner(train)
tree.name = "tree"
 

src/o/r/Orange-2.7.2/docs/reference/rst/code/regression-tree-run.py   Orange(Download)
import Orange
servo = Orange.data.Table("servo.tab")
tree = Orange.regression.tree.TreeLearner(servo)
print tree

src/o/r/Orange-Reliability-0.2.14/orangecontrib/reliability/widgets/OWReliability.py   Orange-Reliability(Download)
    data = data.select(indices, 0)
 
    learner = Orange.regression.tree.TreeLearner()
    w.set_learner(learner)
    w.set_train_data(data)

src/o/r/Orange-2.7.2/Orange/testing/unit/tests/test_tree.py   Orange(Download)
class TestRegression(testing.LearnerTestCase):
    LEARNER = rtree.TreeLearner(max_depth=50)
 
 
@datasets_driven(datasets=testing.CLASSIFICATION_DATASETS)