class documentation

Undocumented

Class Method train Undocumented
Method __init__ Undocumented
Method classify_many Apply self.classify() to each element of featuresets. I.e.:
Method parse_weka_distribution Undocumented
Method parse_weka_output Undocumented
Method prob_classify_many Apply self.prob_classify() to each element of featuresets. I.e.:
Method _classify_many Undocumented
Constant _CLASSIFIER_CLASS Undocumented
Instance Variable _formatter Undocumented
Instance Variable _model Undocumented

Inherited from ClassifierI:

Method classify No summary
Method labels No summary
Method prob_classify No summary
@classmethod
def train(cls, model_filename, featuresets, classifier='naivebayes', options=[], quiet=True): (source)

Undocumented

def __init__(self, formatter, model_filename): (source)

Undocumented

def classify_many(self, featuresets): (source)

Apply self.classify() to each element of featuresets. I.e.:

return [self.classify(fs) for fs in featuresets]
Returns
list(label)Undocumented
def parse_weka_distribution(self, s): (source)

Undocumented

def parse_weka_output(self, lines): (source)

Undocumented

def prob_classify_many(self, featuresets): (source)

Apply self.prob_classify() to each element of featuresets. I.e.:

return [self.prob_classify(fs) for fs in featuresets]
Returns
list(ProbDistI)Undocumented
def _classify_many(self, featuresets, options): (source)

Undocumented

_CLASSIFIER_CLASS: dict[str, str] = (source)

Undocumented

Value
{'naivebayes': 'weka.classifiers.bayes.NaiveBayes',
 'C4.5': 'weka.classifiers.trees.J48',
 'log_regression': 'weka.classifiers.functions.Logistic',
 'svm': 'weka.classifiers.functions.SMO',
 'kstar': 'weka.classifiers.lazy.KStar',
 'ripper': 'weka.classifiers.rules.JRip'}
_formatter = (source)

Undocumented

Undocumented