Uses of Interface
weka.core.Randomizable

Packages that use Randomizable

Package	Description
weka.classifiers
weka.classifiers.functions
weka.classifiers.meta
weka.classifiers.trees
weka.clusterers
weka.core.neighboursearch.balltrees
weka.datagenerators
weka.datagenerators.classifiers.classification
weka.datagenerators.classifiers.regression
weka.datagenerators.clusterers
weka.filters
weka.filters.supervised.instance
weka.filters.unsupervised.attribute
weka.filters.unsupervised.instance

Uses of Randomizable in weka.classifiers

Classes in weka.classifiers that implement Randomizable

Modifier and Type	Class	Description
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.

Uses of Randomizable in weka.classifiers.functions

Classes in weka.classifiers.functions that implement Randomizable

Modifier and Type	Class	Description
class	GaussianProcesses	* Implements Gaussian processes for regression without hyperparameter-tuning.
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.

Uses of Randomizable in weka.classifiers.meta

Classes in weka.classifiers.meta that implement Randomizable

Modifier and Type	Class	Description
class	AdaBoostM1	Class for boosting a nominal class classifier using the Adaboost M1 method.
class	Bagging	Class for bagging a classifier to reduce variance.
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	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 Randomizable in weka.classifiers.trees

Classes in weka.classifiers.trees that implement Randomizable

Modifier and Type	Class	Description
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 Randomizable in weka.clusterers

Classes in weka.clusterers that implement Randomizable

Modifier and Type	Class	Description
class	Canopy	Cluster data using the capopy clustering algorithm, which requires just one pass over the data.
class	Cobweb	Class implementing the Cobweb and Classit clustering algorithms. Note: the application of node operators (merging, splitting etc.) in terms of ordering and priority differs (and is somewhat ambiguous) between the original Cobweb and Classit papers.
class	EM	Simple EM (expectation maximisation) class. EM assigns a probability distribution to each instance which indicates the probability of it belonging to each of the clusters.
class	FarthestFirst	Cluster data using the FarthestFirst algorithm. For more information see: Hochbaum, Shmoys (1985).
class	RandomizableClusterer	Abstract utility class for handling settings common to randomizable clusterers.
class	RandomizableDensityBasedClusterer	Abstract utility class for handling settings common to randomizable clusterers.
class	RandomizableSingleClustererEnhancer	Abstract utility class for handling settings common to randomizable clusterers.
class	SimpleKMeans	Cluster data using the k means algorithm.

Uses of Randomizable in weka.core.neighboursearch.balltrees

Classes in weka.core.neighboursearch.balltrees that implement Randomizable

Modifier and Type	Class	Description
class	MiddleOutConstructor	The class that builds a BallTree middle out. For more information see also: Andrew W.

Uses of Randomizable in weka.datagenerators

Classes in weka.datagenerators that implement Randomizable

Modifier and Type	Class	Description
class	ClassificationGenerator	Abstract class for data generators for classifiers.
class	ClusterGenerator	Abstract class for cluster data generators.
class	DataGenerator	Abstract superclass for data generators that generate data for classifiers and clusterers.
class	RegressionGenerator	Abstract class for data generators for regression classifiers.

Uses of Randomizable in weka.datagenerators.classifiers.classification

Classes in weka.datagenerators.classifiers.classification that implement Randomizable

Modifier and Type	Class	Description
class	Agrawal	Generates a people database and is based on the paper by Agrawal et al.: R.
class	BayesNet	Generates random instances based on a Bayes network.
class	LED24	This generator produces data for a display with 7 LEDs.
class	RandomRBF	RandomRBF data is generated by first creating a random set of centers for each class.
class	RDG1	A data generator that produces data randomly by producing a decision list. The decision list consists of rules. Instances are generated randomly one by one.

Uses of Randomizable in weka.datagenerators.classifiers.regression

Classes in weka.datagenerators.classifiers.regression that implement Randomizable

Modifier and Type	Class	Description
class	Expression	A data generator for generating y according to a given expression out of randomly generated x. E.g., the mexican hat can be generated like this: sin(abs(a1)) / abs(a1) In addition to this function, the amplitude can be changed and gaussian noise can be added.
class	MexicanHat	A data generator for the simple 'Mexian Hat' function: y = sin|x| / |x| In addition to this simple function, the amplitude can be changed and gaussian noise can be added.

Uses of Randomizable in weka.datagenerators.clusterers

Classes in weka.datagenerators.clusterers that implement Randomizable

Modifier and Type	Class	Description
class	BIRCHCluster	Cluster data generator designed for the BIRCH System Dataset is generated with instances in K clusters. Instances are 2-d data points. Each cluster is characterized by the number of data points in itits radius and its center.
class	SubspaceCluster	A data generator that produces data points in hyperrectangular subspace clusters.

Uses of Randomizable in weka.filters

Classes in weka.filters that implement Randomizable

Modifier and Type	Class	Description
class	MultiFilter	Applies several filters successively.

Uses of Randomizable in weka.filters.supervised.instance

Classes in weka.filters.supervised.instance that implement Randomizable

Modifier and Type	Class	Description
class	Resample	Produces a random subsample of a dataset using either sampling with replacement or without replacement. The original dataset must fit entirely in memory.
class	SpreadSubsample	Produces a random subsample of a dataset.

Uses of Randomizable in weka.filters.unsupervised.attribute

Classes in weka.filters.unsupervised.attribute that implement Randomizable

Modifier and Type	Class	Description
class	AddNoise	An instance filter that changes a percentage of a given attribute's values.
class	RandomProjection	Reduces the dimensionality of the data by projecting it onto a lower dimensional subspace using a random matrix with columns of unit length.
class	RandomSubset	Chooses a random subset of non-class attributes, either an absolute number or a percentage.
class	ReplaceWithMissingValue	A filter that can be used to introduce missing values in a dataset.

Uses of Randomizable in weka.filters.unsupervised.instance

Classes in weka.filters.unsupervised.instance that implement Randomizable

Modifier and Type	Class	Description
class	Randomize	Randomly shuffles the order of instances passed through it.
class	Resample	Produces a random subsample of a dataset using either sampling with replacement or without replacement.
class	ReservoirSample	Produces a random subsample of a dataset using the reservoir sampling Algorithm "R" by Vitter.