Uses of Class
weka.classifiers.AbstractClassifier

Packages that use AbstractClassifier

Package	Description
weka.classifiers
weka.classifiers.bayes
weka.classifiers.bayes.net
weka.classifiers.functions
weka.classifiers.lazy
weka.classifiers.meta
weka.classifiers.misc
weka.classifiers.pmml.consumer
weka.classifiers.rules
weka.classifiers.trees
weka.classifiers.trees.lmt
weka.classifiers.trees.m5

Uses of AbstractClassifier in weka.classifiers

Subclasses of AbstractClassifier in weka.classifiers

Modifier and Type	Class	Description
class	IteratedSingleClassifierEnhancer	Abstract utility class for handling settings common to meta classifiers that build an ensemble from a single base learner.
class	MultipleClassifiersCombiner	Abstract utility class for handling settings common to meta classifiers that build an ensemble from multiple classifiers.
class	ParallelIteratedSingleClassifierEnhancer	Abstract utility class for handling settings common to meta classifiers that build an ensemble in parallel from a single base learner.
class	ParallelMultipleClassifiersCombiner	Abstract utility class for handling settings common to meta classifiers that build an ensemble in parallel using multiple classifiers.
class	RandomizableClassifier	Abstract utility class for handling settings common to randomizable classifiers.
class	RandomizableIteratedSingleClassifierEnhancer	Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
class	RandomizableMultipleClassifiersCombiner	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.
class	RandomizableParallelIteratedSingleClassifierEnhancer	Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble in parallel from a single base learner.
class	RandomizableParallelMultipleClassifiersCombiner	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.
class	RandomizableSingleClassifierEnhancer	Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
class	SingleClassifierEnhancer	Abstract utility class for handling settings common to meta classifiers that use a single base learner.

Uses of AbstractClassifier in weka.classifiers.bayes

Subclasses of AbstractClassifier in weka.classifiers.bayes

Modifier and Type	Class	Description
class	BayesNet	Bayes Network learning using various search algorithms and quality measures. Base class for a Bayes Network classifier.
class	NaiveBayes	Class for a Naive Bayes classifier using estimator classes.
class	NaiveBayesMultinomial	Class for building and using a multinomial Naive Bayes classifier.
class	NaiveBayesMultinomialText	Multinomial naive bayes for text data.
class	NaiveBayesMultinomialUpdateable	Class for building and using an updateable multinomial Naive Bayes classifier.
class	NaiveBayesUpdateable	Class for a Naive Bayes classifier using estimator classes.

Uses of AbstractClassifier in weka.classifiers.bayes.net

Subclasses of AbstractClassifier in weka.classifiers.bayes.net

Modifier and Type	Class	Description
class	BayesNetGenerator	Bayes Network learning using various search algorithms and quality measures. Base class for a Bayes Network classifier.
class	BIFReader	Builds a description of a Bayes Net classifier stored in XML BIF 0.3 format. For more details on XML BIF see: Fabio Cozman, Marek Druzdzel, Daniel Garcia (1998).
class	EditableBayesNet	Bayes Network learning using various search algorithms and quality measures. Base class for a Bayes Network classifier.

Uses of AbstractClassifier in weka.classifiers.functions

Subclasses of AbstractClassifier in weka.classifiers.functions

Modifier and Type	Class	Description
class	GaussianProcesses	* Implements Gaussian processes for regression without hyperparameter-tuning.
class	LinearRegression	Class for using linear regression for prediction.
class	Logistic	Class for building and using a multinomial logistic regression model with a ridge estimator. There are some modifications, however, compared to the paper of leCessie and van Houwelingen(1992): If there are k classes for n instances with m attributes, the parameter matrix B to be calculated will be an m*(k-1) matrix. The probability for class j with the exception of the last class is Pj(Xi) = exp(XiBj)/((sum[j=1..(k-1)]exp(Xi*Bj))+1) The last class has probability 1-(sum[j=1..(k-1)]Pj(Xi)) = 1/((sum[j=1..(k-1)]exp(Xi*Bj))+1) The (negative) multinomial log-likelihood is thus: L = -sum[i=1..n]{ sum[j=1..(k-1)](Yij * ln(Pj(Xi))) +(1 - (sum[j=1..(k-1)]Yij)) * ln(1 - sum[j=1..(k-1)]Pj(Xi)) } + ridge * (B^2) In order to find the matrix B for which L is minimised, a Quasi-Newton Method is used to search for the optimized values of the m*(k-1) variables.
class	MultilayerPerceptron	A classifier that uses backpropagation to learn a multi-layer perceptron to classify instances.
class	SGD	Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression, squared loss, Huber loss and epsilon-insensitive loss linear regression).
class	SGDText	Implements stochastic gradient descent for learning a linear binary class SVM or binary class logistic regression on text data.
class	SimpleLinearRegression	Learns a simple linear regression model.
class	SimpleLogistic	Classifier for building linear logistic regression models.
class	SMO	Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
class	SMOreg	SMOreg implements the support vector machine for regression.
class	VotedPerceptron	Implementation of the voted perceptron algorithm by Freund and Schapire.

Uses of AbstractClassifier in weka.classifiers.lazy

Subclasses of AbstractClassifier in weka.classifiers.lazy

Modifier and Type	Class	Description
class	IBk	K-nearest neighbours classifier.
class	KStar	K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function.
class	LWL	Locally weighted learning.

Uses of AbstractClassifier in weka.classifiers.meta

Subclasses of AbstractClassifier in weka.classifiers.meta

Modifier and Type	Class	Description
class	AdaBoostM1	Class for boosting a nominal class classifier using the Adaboost M1 method.
class	AdditiveRegression	Meta classifier that enhances the performance of a regression base classifier.
class	AttributeSelectedClassifier	Dimensionality of training and test data is reduced by attribute selection before being passed on to a classifier.
class	Bagging	Class for bagging a classifier to reduce variance.
class	ClassificationViaRegression	Class for doing classification using regression methods.
class	CostSensitiveClassifier	A metaclassifier that makes its base classifier cost sensitive.
class	CVParameterSelection	Class for performing parameter selection by cross-validation for any classifier. For more information, see: R.
class	FilteredClassifier	Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
class	IterativeClassifierOptimizer	Chooses the best number of iterations for an IterativeClassifier such as LogitBoost using cross-validation or a percentage split evaluation.
class	LogitBoost	Class for performing additive logistic regression.
class	MultiClassClassifier	A metaclassifier for handling multi-class datasets with 2-class classifiers.
class	MultiClassClassifierUpdateable	A metaclassifier for handling multi-class datasets with 2-class classifiers.
class	MultiScheme	Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
class	RandomCommittee	Class for building an ensemble of randomizable base classifiers.
class	RandomizableFilteredClassifier	Class for running an arbitrary classifier on data that has been passed through an arbitrary filter.
class	RandomSubSpace	This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
class	RegressionByDiscretization	A regression scheme that employs any classifier on a copy of the data that has the class attribute (equal-width) discretized.
class	Stacking	Combines several classifiers using the stacking method.
class	Vote	Class for combining classifiers.
class	WeightedInstancesHandlerWrapper	Generic wrapper around any classifier to enable weighted instances support. Uses resampling with weights if the base classifier is not implementing the weka.core.WeightedInstancesHandler interface and there are instance weights other 1.0 present.

Uses of AbstractClassifier in weka.classifiers.misc

Subclasses of AbstractClassifier in weka.classifiers.misc

Modifier and Type	Class	Description
class	InputMappedClassifier	Wrapper classifier that addresses incompatible training and test data by building a mapping between the training data that a classifier has been built with and the incoming test instances' structure.
class	SerializedClassifier	A wrapper around a serialized classifier model.

Uses of AbstractClassifier in weka.classifiers.pmml.consumer

Subclasses of AbstractClassifier in weka.classifiers.pmml.consumer

Modifier and Type	Class	Description
class	GeneralRegression	Class implementing import of PMML General Regression model.
class	NeuralNetwork	Class implementing import of PMML Neural Network model.
class	PMMLClassifier	Abstract base class for all PMML classifiers.
class	Regression	Class implementing import of PMML Regression model.
class	RuleSetModel	Class implementing import of PMML RuleSetModel.
class	SupportVectorMachineModel	Implements a PMML SupportVectorMachineModel
class	TreeModel	Class implementing import of PMML TreeModel.

Uses of AbstractClassifier in weka.classifiers.rules

Subclasses of AbstractClassifier in weka.classifiers.rules

Modifier and Type	Class	Description
class	DecisionTable	Class for building and using a simple decision table majority classifier. For more information see: Ron Kohavi: The Power of Decision Tables.
class	JRip	This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W.
class	M5Rules	Generates a decision list for regression problems using separate-and-conquer.
class	OneR	Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes.
class	PART	Class for generating a PART decision list.
class	ZeroR	Class for building and using a 0-R classifier.

Uses of AbstractClassifier in weka.classifiers.trees

Subclasses of AbstractClassifier in weka.classifiers.trees

Modifier and Type	Class	Description
class	DecisionStump	Class for building and using a decision stump.
class	HoeffdingTree	A Hoeffding tree (VFDT) is an incremental, anytime decision tree induction algorithm that is capable of learning from massive data streams, assuming that the distribution generating examples does not change over time.
class	J48	Class for generating a pruned or unpruned C4.5 decision tree.
class	LMT	Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.
class	M5P	M5Base.
class	RandomForest	Class for constructing a forest of random trees. For more information see: Leo Breiman (2001).
class	RandomTree	Class for constructing a tree that considers K randomly chosen attributes at each node.
class	REPTree	Fast decision tree learner.

Uses of AbstractClassifier in weka.classifiers.trees.lmt

Subclasses of AbstractClassifier in weka.classifiers.trees.lmt

Modifier and Type	Class	Description
class	LMTNode	Class for logistic model tree structure.
class	LogisticBase	Base/helper class for building logistic regression models with the LogitBoost algorithm.

Uses of AbstractClassifier in weka.classifiers.trees.m5

Subclasses of AbstractClassifier in weka.classifiers.trees.m5

Modifier and Type	Class	Description
class	M5Base	M5Base.
class	PreConstructedLinearModel	This class encapsulates a linear regression function.
class	RuleNode	Constructs a node for use in an m5 tree or rule