# 1. cheating with default filter
fltr = Filter(classname="weka.filters.supervised.attribute.Discretize",
options=[])
fltr.inputformat(data)
filtered = fltr.filter(data)
cls = Classifier(classname="weka.classifiers.trees.J48")
evl = Evaluation(filtered)
evl.crossvalidate_model(cls, filtered, 10, Random(1))
cls.build_classifier(filtered)
print("cheating (default): accuracy=%0.1f nodes=%s" %
(evl.percent_correct, get_nodes(str(cls))))
# 2. using FilteredClassifier with default filter
cls = FilteredClassifier()
cls.classifier = Classifier(classname="weka.classifiers.trees.J48")
cls.filter = Filter(classname="weka.filters.supervised.attribute.Discretize",
options=[])
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(1))
cls.build_classifier(data)
print("FilteredClassifier (default): accuracy=%0.1f nodes=%s" %
(evl.percent_correct, get_nodes(str(cls))))
# 3. using FilteredClassifier (make binary)
cls = FilteredClassifier()
cls.classifier = Classifier(classname="weka.classifiers.trees.J48")
cls.filter = Filter(classname="weka.filters.supervised.attribute.Discretize",
options=["-D"])
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(1))
cls.build_classifier(data)
print("FilteredClassifier (make binary): accuracy=%0.1f nodes=%s" %
data.class_is_last()
# 1. cheating with default filter
fltr = Filter(classname="weka.filters.supervised.attribute.Discretize", options=[])
fltr.inputformat(data)
filtered = fltr.filter(data)
cls = Classifier(classname="weka.classifiers.trees.J48")
evl = Evaluation(filtered)
evl.crossvalidate_model(cls, filtered, 10, Random(1))
cls.build_classifier(filtered)
print("cheating (default): accuracy=%0.1f nodes=%s" % (evl.percent_correct, get_nodes(str(cls))))
# 2. using FilteredClassifier with default filter
cls = FilteredClassifier()
cls.classifier = Classifier(classname="weka.classifiers.trees.J48")
cls.filter = Filter(classname="weka.filters.supervised.attribute.Discretize", options=[])
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(1))
cls.build_classifier(data)
print("FilteredClassifier (default): accuracy=%0.1f nodes=%s" % (evl.percent_correct, get_nodes(str(cls))))
# 3. using FilteredClassifier (make binary)
cls = FilteredClassifier()
cls.classifier = Classifier(classname="weka.classifiers.trees.J48")
cls.filter = Filter(classname="weka.filters.supervised.attribute.Discretize", options=["-D"])
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(1))
cls.build_classifier(data)
print("FilteredClassifier (make binary): accuracy=%0.1f nodes=%s" % (evl.percent_correct, get_nodes(str(cls))))
# 1. cheating with make binary
