Uses of Interface
de.jungblut.classification.Classifier

Packages that use Classifier

Package	Description
de.jungblut.classification
de.jungblut.classification.bayes
de.jungblut.classification.eval
de.jungblut.classification.knn
de.jungblut.classification.meta
de.jungblut.classification.nn
de.jungblut.classification.regression
de.jungblut.classification.tree
de.jungblut.ner
de.jungblut.nlp

Uses of Classifier in de.jungblut.classification

Classes in de.jungblut.classification with type parameters of type Classifier

Modifier and Type	Interface and Description
interface	ClassifierFactory<A extends Classifier> Factory interface for building new classifiers, majorly used in crossvalidation to generate new classifiers when needed.

Classes in de.jungblut.classification that implement Classifier

Modifier and Type	Class and Description
class	AbstractClassifier Abstract base class for classifiers.
class	UntrainableClassifier

Methods in de.jungblut.classification that return Classifier

Modifier and Type	Method and Description
default Classifier	Predictor.asClassifier() Backward compatibility method to make online-ml project's predictors work with almost everything in this library.

Uses of Classifier in de.jungblut.classification.bayes

Classes in de.jungblut.classification.bayes that implement Classifier

Modifier and Type	Class and Description
class	MultinomialNaiveBayes Multinomial naive bayes classifier.

Uses of Classifier in de.jungblut.classification.eval

Classes in de.jungblut.classification.eval with type parameters of type Classifier

Modifier and Type	Class and Description
class	EvaluationListener<A extends Classifier> The evaluation listener is majorly used to track the overfitting of a classifier while training.
class	TestSetIterationCallback<T extends Classifier> This callback is used to evaluate the performance on a held-out test set.
interface	WeightMapper<A extends Classifier> This interface helps to map minimizable weights of a CostFunction to a Classifier implementation.

Methods in de.jungblut.classification.eval with type parameters of type Classifier

Modifier and Type	Method and Description
static <A extends Classifier> Evaluator.EvaluationResult	Evaluator.crossValidateClassifier(ClassifierFactory<A> classifierFactory, de.jungblut.math.DoubleVector[] features, de.jungblut.math.DoubleVector[] outcome, int numLabels, int folds, Double threshold, boolean verbose) Does a k-fold crossvalidation on the given classifiers with features and outcomes.
static <A extends Classifier> Evaluator.EvaluationResult	Evaluator.crossValidateClassifier(ClassifierFactory<A> classifierFactory, de.jungblut.math.DoubleVector[] features, de.jungblut.math.DoubleVector[] outcome, int numLabels, int folds, Double threshold, int numThreads, boolean verbose) Does a k-fold crossvalidation on the given classifiers with features and outcomes.
static <A extends Classifier> Evaluator.EvaluationResult	Evaluator.tenFoldCrossValidation(ClassifierFactory<A> classifierFactory, de.jungblut.math.DoubleVector[] features, de.jungblut.math.DoubleVector[] outcome, int numLabels, Double threshold, boolean verbose) Does a 10 fold crossvalidation.
static <A extends Classifier> Evaluator.EvaluationResult	Evaluator.tenFoldCrossValidation(ClassifierFactory<A> classifierFactory, de.jungblut.math.DoubleVector[] features, de.jungblut.math.DoubleVector[] outcome, int numLabels, Double threshold, int numThreads, boolean verbose) Does a 10 fold crossvalidation.

Methods in de.jungblut.classification.eval with parameters of type Classifier

Modifier and Type	Method and Description
static Evaluator.EvaluationResult	Evaluator.evaluateClassifier(Classifier classifier, de.jungblut.math.DoubleVector[] features, de.jungblut.math.DoubleVector[] outcome, float splitFraction, boolean random) Trains and evaluates the given classifier with a test split.
static Evaluator.EvaluationResult	Evaluator.evaluateClassifier(Classifier classifier, de.jungblut.math.DoubleVector[] features, de.jungblut.math.DoubleVector[] outcome, float splitFraction, boolean random, Double threshold) Trains and evaluates the given classifier with a test split.
static Evaluator.EvaluationResult	Evaluator.evaluateSplit(Classifier classifier, de.jungblut.math.DoubleVector[] trainFeatures, de.jungblut.math.DoubleVector[] trainOutcome, de.jungblut.math.DoubleVector[] testFeatures, de.jungblut.math.DoubleVector[] testOutcome, Double threshold) Evaluates a given train/test split with the given classifier.
static Evaluator.EvaluationResult	Evaluator.evaluateSplit(Classifier classifier, EvaluationSplit split) Evaluates a given train/test split with the given classifier.
static Evaluator.EvaluationResult	Evaluator.evaluateSplit(Classifier classifier, EvaluationSplit split, Double threshold) Evaluates a given train/test split with the given classifier.

Uses of Classifier in de.jungblut.classification.knn

Classes in de.jungblut.classification.knn that implement Classifier

Modifier and Type	Class and Description
class	AbstractKNearestNeighbours K nearest neighbour classification algorithm that is seeded with a "database" of known examples and predicts based on the k-nearest neighbours majority vote for a class.
class	KNearestNeighbours K nearest neighbour classification algorithm that is seeded with a "database" of known examples and predicts based on the k-nearest neighbours majority vote for a class.
class	SparseKNearestNeighbours K nearest neighbour classification algorithm that is seeded with a "database" of known examples and predicts based on the k-nearest neighbours majority vote for a class.

Uses of Classifier in de.jungblut.classification.meta

Classes in de.jungblut.classification.meta with type parameters of type Classifier

Modifier and Type	Class and Description
class	Voter<A extends Classifier> Implementation of vote ensembling.

Classes in de.jungblut.classification.meta that implement Classifier

Modifier and Type	Class and Description
class	Voter<A extends Classifier> Implementation of vote ensembling.

Methods in de.jungblut.classification.meta with type parameters of type Classifier

Modifier and Type	Method and Description
static <K extends Classifier> Voter<K>	Voter.create(int numClassifiers, Voter.CombiningType type, ClassifierFactory<K> classifierFactory) Creates a new voting classificator.
static <K extends Classifier> Voter<K>	Voter.fromTrainedModels(List<K> classifier) Creates a voter from the given trained models for prediction purposes.

Methods in de.jungblut.classification.meta that return Classifier

Modifier and Type	Method and Description
Classifier[]	Voter.getClassifier()

Uses of Classifier in de.jungblut.classification.nn

Classes in de.jungblut.classification.nn that implement Classifier

Modifier and Type	Class and Description
class	MultilayerPerceptron Multilayer perceptron implementation that works on GPU via JCuda and CPU.

Uses of Classifier in de.jungblut.classification.regression

Classes in de.jungblut.classification.regression that implement Classifier

Modifier and Type	Class and Description
class	LogisticRegression

Uses of Classifier in de.jungblut.classification.tree

Classes in de.jungblut.classification.tree that implement Classifier

Modifier and Type	Class and Description
class	DecisionTree A decision tree that can be used for classification with numerical or categorical features.
class	RandomForest A decision tree forest, using bagging.

Uses of Classifier in de.jungblut.ner

Classes in de.jungblut.ner that implement Classifier

Modifier and Type	Class and Description
class	MaxEntMarkovModel Maximum entropy markov model for named entity recognition (classifying labels in sequence learning).

Uses of Classifier in de.jungblut.nlp

Classes in de.jungblut.nlp that implement Classifier

Modifier and Type	Class and Description
class	HMM Hidden Markov Model implementation for multiple observations for all three types of problems HMM aims to solve (Decoding, likelihood estimation, unsupervised/supervised learning).