miml.classifier.mi.mi_wrapper_classifier.MIWrapperClassifier

miml.classifier.mi.mi_wrapper_classifier.MIWrapperClassifier#

class miml.classifier.mi.mi_wrapper_classifier.MIWrapperClassifier(base_classifier=DecisionTreeClassifier())#

MIWrapper Classifier.

A simple Wrapper method for applying standard propositional learners to multi-instance data.

Attributes#

base_classifier

Classifier to be used

References#

E. T. Frank, X. Xu (2003). Applying propositional learning algorithms to multi-instance data. Department of Computer Science, University of Waikato, Hamilton, NZ.

__init__(base_classifier=DecisionTreeClassifier())#

Methods

__init__([base_classifier])

fit(x_train, y_train[, weight])

Fit the classifier to the training data.

predict(bag)

Predict the label of the bag

predict_proba(x_test)

Predict probabilities of given data of having a positive label