Package weka.classifiers
package weka.classifiers
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ClassDescriptionAbstract classifier.Subclass of Evaluation that provides a method for aggregating the results stored in another Evaluation object.Class for performing a Bias-Variance decomposition on any classifier using the method specified in:
Ron Kohavi, David H.This class performs Bias-Variance decomposion on any classifier using the sub-sampled cross-validation procedure as specified in (1).
The Kohavi and Wolpert definition of bias and variance is specified in (2).
The Webb definition of bias and variance is specified in (3).
Geoffrey I.Class for examining the capabilities and finding problems with classifiers.A simple class for checking the source generated from Classifiers implementing theweka.classifiers.Sourcableinterface.Classifier interface.Interface for numeric prediction schemes that can output conditional density estimates.Class for storing and manipulating a misclassification cost matrix.Class for evaluating machine learning models.Interface for numeric prediction schemes that can output prediction intervals.Abstract utility class for handling settings common to meta classifiers that build an ensemble from a single base learner.Interface for classifiers that can induce models of growing complexity one step at a time.Abstract utility class for handling settings common to meta classifiers that build an ensemble from multiple classifiers.Abstract utility class for handling settings common to meta classifiers that build an ensemble in parallel from a single base learner.Abstract utility class for handling settings common to meta classifiers that build an ensemble in parallel using multiple classifiers.Abstract utility class for handling settings common to randomizable classifiers.Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from multiple classifiers based on a given random number seed.Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble in parallel from a single base learner.Abstract utility class for handling settings common to meta classifiers that build an ensemble in parallel using multiple classifiers based on a given random number seed.Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.Abstract utility class for handling settings common to meta classifiers that use a single base learner.Interface for classifiers that can be converted to Java source.Updateable classifiers can implement this if they wish to be informed at the end of the training stream.Interface to incremental classification models that can learn using one instance at a time.