Uses of Class
de.jungblut.classification.eval.Evaluator.EvaluationResult

Packages that use Evaluator.EvaluationResult

Package	Description
de.jungblut.classification.eval

Uses of Evaluator.EvaluationResult in de.jungblut.classification.eval

Methods in de.jungblut.classification.eval that return Evaluator.EvaluationResult

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 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.
Evaluator.EvaluationResult	TestSetIterationCallback.getBestResult()
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.
static Evaluator.EvaluationResult	Evaluator.testClassifier(Predictor classifier, de.jungblut.math.DoubleVector[] testFeatures, de.jungblut.math.DoubleVector[] testOutcome) Tests the given classifier without actually training it.
static Evaluator.EvaluationResult	Evaluator.testClassifier(Predictor classifier, de.jungblut.math.DoubleVector[] testFeatures, de.jungblut.math.DoubleVector[] testOutcome, Double threshold) Tests the given classifier without actually training it.

Methods in de.jungblut.classification.eval with parameters of type Evaluator.EvaluationResult

Modifier and Type	Method and Description
void	Evaluator.EvaluationResult.add(Evaluator.EvaluationResult res)
static int	Evaluator.observeBinaryClassificationElement(Predictor predictor, Double threshold, Evaluator.EvaluationResult result, de.jungblut.math.DoubleVector outcomeVector, de.jungblut.math.DoubleVector predictedVector)
protected void	EvaluationListener.onResult(int iteration, double cost, Evaluator.EvaluationResult trainEval, Evaluator.EvaluationResult testEval) Will be called on a result of the evaluation.

Constructor parameters in de.jungblut.classification.eval with type arguments of type Evaluator.EvaluationResult

Constructor and Description
TestSetIterationCallback(EvaluationSplit split, WeightMapper<T> mapper, Comparator<Evaluator.EvaluationResult> resultComparison) Creates a new test set iteration callback instance.
TestSetIterationCallback(EvaluationSplit split, WeightMapper<T> mapper, Comparator<Evaluator.EvaluationResult> resultComparison, int evaluationInterval) Creates a new test set iteration callback instance.