Package deepnetts.eval

Class RegresionEvaluator

java.lang.Object

deepnetts.eval.RegresionEvaluator

All Implemented Interfaces:

javax.visrec.ml.eval.Evaluator<NeuralNetwork,javax.visrec.ml.data.DataSet<? extends MLDataItem>>

public class RegresionEvaluator
extends Object
implements javax.visrec.ml.eval.Evaluator<NeuralNetwork,javax.visrec.ml.data.DataSet<? extends MLDataItem>>

Evaluates regressor neural network for specified data set. Assumes only one output at the moment. TODO: f statistic https://www.statisticshowto.com/probability-and-statistics/f-statistic-value-test/

Constructor Summary

Constructors

Constructor	Description
RegresionEvaluator()

Method Summary

Modifier and Type	Method	Description
javax.visrec.ml.eval.EvaluationMetrics	evaluate(NeuralNetwork neuralNet, javax.visrec.ml.data.DataSet<? extends MLDataItem> testSet)
static javax.visrec.ml.eval.EvaluationMetrics	macroAverage(Collection<javax.visrec.ml.eval.EvaluationMetrics> metrics)

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

RegresionEvaluator

public RegresionEvaluator()

Method Details

evaluate

public javax.visrec.ml.eval.EvaluationMetrics evaluate(NeuralNetwork neuralNet,
 javax.visrec.ml.data.DataSet<? extends MLDataItem> testSet)

Specified by:

evaluate in interface javax.visrec.ml.eval.Evaluator<NeuralNetwork,javax.visrec.ml.data.DataSet<? extends MLDataItem>>

macroAverage

public static javax.visrec.ml.eval.EvaluationMetrics macroAverage(Collection<javax.visrec.ml.eval.EvaluationMetrics> metrics)