__init__(label[, feature_name, decisions, ...]) |
| param label: | The most likely label for tokens that reach |
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best_binary_stump(feature_names, ...[, verbose]) |
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best_stump(feature_names, labeled_featuresets) |
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binary_stump(feature_name, feature_value, ...) |
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classify(featureset) |
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classify_many(featuresets) |
Apply self.classify() to each element of featuresets. |
error(labeled_featuresets) |
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labels() |
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leaf(labeled_featuresets) |
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pretty_format([width, prefix, depth]) |
Return a string containing a pretty-printed version of this decision tree. |
prob_classify(featureset) |
| return: | a probability distribution over labels for the given |
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prob_classify_many(featuresets) |
Apply self.prob_classify() to each element of featuresets. |
pseudocode([prefix, depth]) |
Return a string representation of this decision tree that expresses the decisions it makes as a nested set of pseudocode if statements. |
refine(labeled_featuresets, entropy_cutoff, ...) |
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stump(feature_name, labeled_featuresets) |
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train(labeled_featuresets[, entropy_cutoff, ...]) |
| param binary: | If true, then treat all feature/value pairs as |
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