nltk.classify.positivenaivebayes.PositiveNaiveBayesClassifier

class documentation

class PositiveNaiveBayesClassifier(NaiveBayesClassifier): (source)

Undocumented

Static Method train No summary

Method	`__init__`	No summary
Method	`classify`	No summary
Method	`labels`	No summary
Method	`most_informative_features`	Return a list of the 'most informative' features used by this classifier. For the purpose of this function, the informativeness of a feature `(fname,fval)` is equal to the highest value of P(fname=fval\|label), for any label, divided by the lowest value of P(fname=fval\|label), for any label:...
Method	`prob_classify`	No summary
Method	`show_most_informative_features`	Undocumented
Instance Variable	`_feature_probdist`	Undocumented
Instance Variable	`_label_probdist`	Undocumented
Instance Variable	`_labels`	Undocumented
Instance Variable	`_most_informative_features`	Undocumented

Method	`classify_many`	Apply `self.classify()` to each element of `featuresets`. I.e.:
Method	`prob_classify_many`	Apply `self.prob_classify()` to each element of `featuresets`. I.e.:

@staticmethod
def train(positive_featuresets, unlabeled_featuresets, positive_prob_prior=0.5, estimator=ELEProbDist): (source) ¶

overrides nltk.classify.naivebayes.NaiveBayesClassifier.train

Parameters
positive_featuresets	An iterable of featuresets that are known as positive examples (i.e., their label is `True`).
unlabeled_featuresets	An iterable of featuresets whose label is unknown.
positive_prob_prior	A prior estimate of the probability of the label `True` (default 0.5).
estimator	Undocumented