Uses of Interface
jsat.regression.Regressor

Packages that use Regressor

Package	Description
jsat.classifiers
jsat.classifiers.boosting
jsat.classifiers.knn
jsat.classifiers.linear
jsat.classifiers.linear.kernelized
jsat.classifiers.neuralnetwork
jsat.classifiers.svm
jsat.classifiers.trees
jsat.datatransform
jsat.datatransform.featureselection
jsat.parameters
jsat.regression

Uses of Regressor in jsat.classifiers

Constructors in jsat.classifiers with parameters of type Regressor

Constructor and Description
RegressorToClassifier(Regressor regressor) Creates a new Binary Classifier by using the given regressor

Uses of Regressor in jsat.classifiers.boosting

Classes in jsat.classifiers.boosting that implement Regressor

Modifier and Type	Class and Description
class	Bagging An implementation of Bootstrap Aggregating, as described by LEO BREIMAN in "Bagging Predictors".
class	Stacking This provides an implementation of the Stacking ensemble method.
class	UpdatableStacking This provides an implementation of the Stacking ensemble method meant for Updatable models.
class	Wagging Wagging is a meta-classifier that is related to Bagging.
class	WaggingNormal Wagging using the Normal distribution.

Fields in jsat.classifiers.boosting declared as Regressor

Modifier and Type	Field and Description
protected Regressor	LogitBoost.baseLearner Weak learner to use, 'the oracle'

Fields in jsat.classifiers.boosting with type parameters of type Regressor

Modifier and Type	Field and Description
protected List<Regressor>	LogitBoost.baseLearners Weak learners

Methods in jsat.classifiers.boosting that return Regressor

Modifier and Type	Method and Description
Regressor	Wagging.getWeakRegressor() Returns the weak learner used for regression

Methods in jsat.classifiers.boosting that return types with arguments of type Regressor

Modifier and Type	Method and Description
List<Regressor>	LogitBoost.getModels()

Methods in jsat.classifiers.boosting with parameters of type Regressor

Modifier and Type	Method and Description
void	Wagging.setWeakLearner(Regressor weakR) Sets the weak learner used for regressions .

Constructors in jsat.classifiers.boosting with parameters of type Regressor

Constructor and Description
Bagging(Regressor baseRegressor) Creates a new Bagger for regression.
Bagging(Regressor baseRegressor, int extraSamples, boolean simultaniousTraining) Creates a new Bagger for regression.
Bagging(Regressor baseRegressor, int extraSamples, boolean simultaniousTraining, int rounds, Random random) Creates a new Bagger for regression.
LogitBoost(Regressor baseLearner, int M) Creates a new LogitBoost using the given base learner.
LogitBoostPL(Regressor baseLearner, int M)
Stacking(int folds, Regressor aggregatingRegressor, List<Regressor> baseRegressors) Creates a new Stacking regressor
Stacking(int folds, Regressor aggregatingRegressor, Regressor... baseRegressors) Creates a new Stacking regressor
Stacking(int folds, Regressor aggregatingRegressor, Regressor... baseRegressors) Creates a new Stacking regressor
Stacking(Regressor aggregatingRegressor, List<Regressor> baseRegressors) Creates a new Stacking regressor that uses 3 folds of cross validation
Stacking(Regressor aggregatingRegressor, Regressor... baseRegressors) Creates a new Stacking regressor that uses 3 folds of cross validation
Stacking(Regressor aggregatingRegressor, Regressor... baseRegressors) Creates a new Stacking regressor that uses 3 folds of cross validation
Wagging(ContinuousDistribution dist, Regressor weakR, int iterations) Creates a new Wagging regressor
WaggingNormal(Regressor weakLearner, int interations) Creates a new Wagging regressor

Constructor parameters in jsat.classifiers.boosting with type arguments of type Regressor

Constructor and Description
Stacking(int folds, Regressor aggregatingRegressor, List<Regressor> baseRegressors) Creates a new Stacking regressor
Stacking(Regressor aggregatingRegressor, List<Regressor> baseRegressors) Creates a new Stacking regressor that uses 3 folds of cross validation

Uses of Regressor in jsat.classifiers.knn

Classes in jsat.classifiers.knn that implement Regressor

Modifier and Type	Class and Description
class	LWL Locally Weighted Learner (LW) is the combined generalized implementation of Locally Weighted Regression (LWR) and Locally Weighted Naive Bayes (LWNB).
class	NearestNeighbour An implementation of the Nearest Neighbor algorithm, but with a British spelling! How fancy.

Constructors in jsat.classifiers.knn with parameters of type Regressor

Constructor and Description
LWL(Regressor regressor, int k, DistanceMetric dm) Creates a new LWL Regressor
LWL(Regressor regressor, int k, DistanceMetric dm, KernelFunction kf) Creates a new LWL Regressor
LWL(Regressor regressor, int k, DistanceMetric dm, KernelFunction kf, VectorCollectionFactory<VecPaired<Vec,Double>> vcf) Creates a new LWL Regressor

Uses of Regressor in jsat.classifiers.linear

Classes in jsat.classifiers.linear that implement Regressor

Modifier and Type	Class and Description
class	LinearBatch LinearBatch learns either a classification or regression problem depending on the loss function ℓ(w,x) used.
class	LinearL1SCD Implements an iterative and single threaded form of fast Stochastic Coordinate Decent for optimizing L1 regularized linear regression problems.
class	LinearSGD LinearSGD learns either a classification or regression problem depending on the loss function ℓ(w,x) used.
class	PassiveAggressive An implementations of the 3 versions of the Passive Aggressive algorithm for binary classification and regression.
class	SCD Implementation of Stochastic Coordinate Descent for L1 regularized classification and regression.
class	SMIDAS Implements the iterative and single threaded stochastic solver for L1 regularized linear regression problems SMIDAS (Stochastic Mirror Descent Algorithm mAde Sparse).
class	STGD This provides an implementation of Sparse Truncated Gradient Descent for L1 regularized linear classification and regression on sparse data sets.
class	StochasticSTLinearL1 This base class provides shared functionality and variables used by two different training algorithms for L1 regularized linear models.

Methods in jsat.classifiers.linear with parameters of type Regressor

Modifier and Type	Method and Description
void	LinearBatch.train(RegressionDataSet dataSet, Regressor warmSolution)
void	LinearBatch.train(RegressionDataSet D, Regressor warmSolution, ExecutorService threadPool)

Uses of Regressor in jsat.classifiers.linear.kernelized

Classes in jsat.classifiers.linear.kernelized that implement Regressor

Modifier and Type	Class and Description
class	KernelSGD Kernel SGD is the kernelized counterpart to LinearSGD, and learns nonlinear functions via the kernel trick.

Uses of Regressor in jsat.classifiers.neuralnetwork

Classes in jsat.classifiers.neuralnetwork that implement Regressor

Modifier and Type	Class and Description
class	BackPropagationNet An implementation of a Feed Forward Neural Network (NN) trained by Back Propagation.
class	RBFNet This provides a highly configurable implementation of a Radial Basis Function Neural Network.

Constructors in jsat.classifiers.neuralnetwork with parameters of type Regressor

Constructor and Description
RBFNet(int numCentroids, RBFNet.Phase1Learner cl, RBFNet.Phase2Learner bl, double alpha, int p, DistanceMetric dm, Regressor baseRegressor) Creates a new RBF Network for regression tasks.

Uses of Regressor in jsat.classifiers.svm

Classes in jsat.classifiers.svm that implement Regressor

Modifier and Type	Class and Description
class	DCD Implements Dual Coordinate Descent (DCD) training algorithms for a Linear L1 or L2 Support Vector Machine for binary classification and regression.
class	DCDs Implements Dual Coordinate Descent with shrinking (DCDs) training algorithms for a Linear L1 or L2 Support Vector Machine for binary classification and regression.
class	LSSVM The Least Squares Support Vector Machine (LS-SVM) is an alternative to the standard SVM classifier for regression and binary classification problems.
class	PlattSMO An implementation of SVMs using Platt's Sequential Minimum Optimization (SMO) for both Classification and Regression problems.

Methods in jsat.classifiers.svm with parameters of type Regressor

Modifier and Type	Method and Description
void	PlattSMO.train(RegressionDataSet dataSet, Regressor warmSolution)
void	LSSVM.train(RegressionDataSet dataSet, Regressor warmSolution)
void	DCDs.train(RegressionDataSet dataSet, Regressor warmSolution)
void	PlattSMO.train(RegressionDataSet dataSet, Regressor warmSolution, ExecutorService threadPool)
void	LSSVM.train(RegressionDataSet dataSet, Regressor warmSolution, ExecutorService threadPool)
void	DCDs.train(RegressionDataSet dataSet, Regressor warmSolution, ExecutorService threadPool)

Uses of Regressor in jsat.classifiers.trees

Classes in jsat.classifiers.trees that implement Regressor

Modifier and Type	Class and Description
class	DecisionStump This class is a 1-rule.
class	DecisionTree Creates a decision tree from DecisionStumps.
class	ERTrees Extra Randomized Trees (ERTrees) is an ensemble method built on top of ExtraTree.
class	ExtraTree The ExtraTree is an Extremely Randomized Tree.
class	RandomDecisionTree An extension of Decision Trees, it ignores the given set of features to use- and selects a new random subset of features at each node for use.
class	RandomForest Random Forest is an extension of Bagging that is applied only to DecisionTrees.

Uses of Regressor in jsat.datatransform

Classes in jsat.datatransform that implement Regressor

Modifier and Type	Class and Description
class	DataModelPipeline A Data Model Pipeline combines several data transforms and a base Classifier or Regressor into a unified object for performing classification and Regression with.

Constructors in jsat.datatransform with parameters of type Regressor

Constructor and Description
DataModelPipeline(Regressor baseRegressor, DataTransform... transforms) Creates a new Data Model Pipeline from the given transform factories and base classifier
DataModelPipeline(Regressor baseRegressor, DataTransformProcess dtp) Creates a new Data Model Pipeline from the given transform process and base regressor

Uses of Regressor in jsat.datatransform.featureselection

Constructors in jsat.datatransform.featureselection with parameters of type Regressor

Constructor and Description
BDS(int featureCount, RegressionDataSet dataSet, Regressor evaluator, int folds) Performs BDS feature selection for a regression problem
BDS(int featureCount, Regressor evaluator, int folds) Creates a BDS feature selection for a regression problem
LRS(int L, int R, RegressionDataSet rds, Regressor evaluater, int folds) Performs LRS feature selection for a regression problem
LRS(int L, int R, Regressor evaluater, int folds) Creates a LRS feature selection object for a regression problem
SBS(int minFeatures, int maxFeatures, RegressionDataSet rds, Regressor evaluater, int folds, double maxDecrease) Performs SBS feature selection for a regression problem
SBS(int minFeatures, int maxFeatures, Regressor evaluater, double maxDecrease) Performs SBS feature selection for a regression problem
SFS(int minFeatures, int maxFeatures, RegressionDataSet dataSet, Regressor regressor, int folds, double maxIncrease) Performs SFS feature selection for a regression problem
SFS(int minFeatures, int maxFeatures, Regressor regressor, double maxIncrease) Creates SFS feature selection for a regression problem

Uses of Regressor in jsat.parameters

Classes in jsat.parameters that implement Regressor

Modifier and Type	Class and Description
class	GridSearch GridSearch is a simple method for tuning the parameters of a classification or regression algorithm.
class	ModelSearch This abstract class provides boilerplate for algorithms that search a model's parameter space to find the parameters that provide the best overall performance.
class	RandomSearch Random Search is a simple method for tuning the parameters of a classification or regression algorithm.

Fields in jsat.parameters declared as Regressor

Modifier and Type	Field and Description
protected Regressor	ModelSearch.baseRegressor
protected Regressor	ModelSearch.trainedRegressor

Methods in jsat.parameters that return Regressor

Modifier and Type	Method and Description
Regressor	ModelSearch.getBaseRegressor() Returns the base regressor that was originally passed in when constructing this GridSearch.
Regressor	ModelSearch.getTrainedRegressor() Returns the resultant regressor trained on the whole data set after performing parameter tuning.

Constructors in jsat.parameters with parameters of type Regressor

Constructor and Description
GridSearch(Regressor baseRegressor, int folds) Creates a new GridSearch to tune the specified parameters of a regression model.
ModelSearch(Regressor baseRegressor, int folds)
RandomSearch(Regressor baseRegressor, int folds) Creates a new GridSearch to tune the specified parameters of a regression model.

Uses of Regressor in jsat.regression

Subinterfaces of Regressor in jsat.regression

Modifier and Type	Interface and Description
interface	UpdateableRegressor UpdateableRegressor is an interface for one type of Online learner.
interface	WarmRegressor This interface is meant for models that support efficient warm starting from the solution of a previous model.

Classes in jsat.regression that implement Regressor

Modifier and Type	Class and Description
class	AveragedRegressor Creates a regressor that averages the results of several voting regression methods.
class	BaseUpdateableRegressor A base implementation of the UpdateableRegressor.
class	KernelRidgeRegression A kernelized implementation of Ridge Regression.
class	KernelRLS Provides an implementation of the Kernel Recursive Least Squares algorithm.
class	LogisticRegression Logistic regression is a common method used to fit a probability between binary outputs.
class	MultipleLinearRegression
class	NadarayaWatson The Nadaraya-Watson regressor uses the Kernel Density Estimator to perform regression on a data set.
class	OrdinaryKriging An implementation of Ordinary Kriging with support for a uniform error measurement.
class	RANSAC RANSAC is a randomized meta algorithm that is useful for fitting a model to a data set that has a large amount of outliers that do not represent the true distribution well.
class	RidgeRegression An implementation of Ridge Regression that finds the exact solution.
class	StochasticGradientBoosting An implementation of Stochastic Gradient Boosting (SGB) for the Squared Error loss.
class	StochasticRidgeRegression A Stochastic implementation of Ridge Regression.

Fields in jsat.regression declared as Regressor

Modifier and Type	Field and Description
protected Regressor[]	AveragedRegressor.voters The array of voting regressors

Methods in jsat.regression that return Regressor

Modifier and Type	Method and Description
Regressor	Regressor.clone()
Regressor	RANSAC.getBaseRegressorClone() Once RANSAC is complete, it maintains its trained version of the finalized regressor.
Regressor[]	RegressionModelEvaluation.getKeptModels() Returns the models that were kept after the last evaluation.
Regressor	RegressionModelEvaluation.getRegressor() Returns the regressor that was to be evaluated

Methods in jsat.regression with parameters of type Regressor

Modifier and Type	Method and Description
void	RegressionModelEvaluation.setWarmModels(Regressor... warmModels) Sets the models that will be used for warm starting training.
void	WarmRegressor.train(RegressionDataSet dataSet, Regressor warmSolution) Trains the regressor and constructs a model for regression using the given data set.
void	WarmRegressor.train(RegressionDataSet dataSet, Regressor warmSolution, ExecutorService threadPool) Trains the regressor and constructs a model for regression using the given data set.

Constructors in jsat.regression with parameters of type Regressor

Constructor and Description
AveragedRegressor(Regressor... voters) Constructs a new averaged regressor using the given array of voters
RANSAC(Regressor baseRegressor, int iterations, int initialTrainSize, int minResultSize, double maxPointError) Creates a new RANSAC training object.
RegressionModelEvaluation(Regressor regressor, RegressionDataSet dataSet) Creates a new RegressionModelEvaluation that will perform serial training
RegressionModelEvaluation(Regressor regressor, RegressionDataSet dataSet, ExecutorService threadpool) Creates a new RegressionModelEvaluation that will perform parallel training.
StochasticGradientBoosting(Regressor weakLearner, int maxIterations) Creates a new SGB learner that is initialized using the weak learner.
StochasticGradientBoosting(Regressor weakLearner, int maxIterations, double learningRate) Creates a new SGB learner that is initialized using the weak learner.
StochasticGradientBoosting(Regressor weakLearner, int maxIterations, double learningRate, double trainingPortion) Creates a new SGB learner that is initialized using the weak learner.
StochasticGradientBoosting(Regressor strongLearner, Regressor weakLearner, int maxIterations, double learningRate, double trainingPortion) Creates a new initialized SGB learner.

Constructor parameters in jsat.regression with type arguments of type Regressor

Constructor and Description
AveragedRegressor(List<Regressor> voters) Constructs a new averaged regressor using the given list of voters.