Package nltk :: Package classify :: Module decisiontree :: Class DecisionTreeClassifier

type DecisionTreeClassifier

     object --+    
              |    
api.ClassifierI --+
                  |
                 DecisionTreeClassifier

Instance Methods

[hide private]

__init__(self, label, feature_name=None, decisions=None, default=None)

source code

list of (immutable)

labels(self)
Returns: the list of category labels used by this classifier.

source code

label

classify(self, featureset)
Returns: the most appropriate label for the given featureset.

source code

error(self, labeled_featuresets)

source code

pp(self, width=70, prefix='', depth=4)
Return a string containing a pretty-printed version of this decision tree. source code

pseudocode(self, prefix='', depth=4)
Return a string representation of this decision tree that expresses the decisions it makes as a nested set of pseudocode if statements. source code

__str__(self)

source code

refine(self, labeled_featuresets, entropy_cutoff, depth_cutoff, support_cutoff, binary=False, feature_values=None, verbose=False)

source code

Inherited from api.ClassifierI: batch_classify, batch_prob_classify, prob_classify

Deprecated

Inherited from api.ClassifierI: batch_probdist, probdist

Static Methods

[hide private]

train(labeled_featuresets, entropy_cutoff=0.05, depth_cutoff=100, support_cutoff=10, binary=False, feature_values=None, verbose=False)

source code

leaf(labeled_featuresets)

source code

stump(feature_name, labeled_featuresets)

source code

best_stump(feature_names, labeled_featuresets, verbose=False)

source code

binary_stump(feature_name, feature_value, labeled_featuresets)

source code

best_binary_stump(feature_names, labeled_featuresets, feature_values, verbose=False)

source code

Method Details

[hide private]

init(self, label, feature_name=None, decisions=None, default=None)
(Constructor)

source code

Parameters:

label - The most likely label for tokens that reach this node in the decision tree. If this decision tree has no children, then this label will be assigned to any token that reaches this decision tree.
feature_name - The name of the feature that this decision tree selects for.
decisions - A dictionary mapping from feature values for the feature identified by feature_name to child decision trees.
default - The child that will be used if the value of feature feature_name does not match any of the keys in decisions. This is used when constructing binary decision trees.

Overrides: object.__init__

labels(self)

source code

Returns: list of (immutable): the list of category labels used by this classifier.
Overrides: api.ClassifierI.labels: (inherited documentation)

classify(self, featureset)

source code

Returns: label: the most appropriate label for the given featureset.
Overrides: api.ClassifierI.classify: (inherited documentation)

pp(self, width=70, prefix=`''`, depth=4)

source code

Return a string containing a pretty-printed version of this decision tree. Each line in this string corresponds to a single decision tree node or leaf, and indentation is used to display the structure of the decision tree.

str(self)
(Informal representation operator)

source code

Overrides: object.__str__: (inherited documentation)

train(labeled_featuresets, entropy_cutoff=0.05, depth_cutoff=100, support_cutoff=10, binary=False, feature_values=None, verbose=False)
Static Method

source code

Parameters:

binary - If true, then treat all feature/value pairs a individual binary features, rather than using a single n-way branch for each feature.

type DecisionTreeClassifier

__init__(self, label, feature_name=None, decisions=None, default=None) (Constructor)

labels(self)

classify(self, featureset)

pp(self, width=70, prefix='', depth=4)

__str__(self) (Informal representation operator)

train(labeled_featuresets, entropy_cutoff=0.05, depth_cutoff=100, support_cutoff=10, binary=False, feature_values=None, verbose=False) Static Method

init(self, label, feature_name=None, decisions=None, default=None)
(Constructor)

pp(self, width=70, prefix=`''`, depth=4)

str(self)
(Informal representation operator)

train(labeled_featuresets, entropy_cutoff=0.05, depth_cutoff=100, support_cutoff=10, binary=False, feature_values=None, verbose=False)
Static Method