Package org.nd4j.evaluation.classification

Class Summary

Class	Description
ConfusionMatrix<T extends Comparable<? super T>>
Evaluation	Evaluation metrics: - precision, recall, f1, fBeta, accuracy, Matthews correlation coefficient, gMeasure - Top N accuracy (if using constructor Evaluation.Evaluation(List, int)) - Custom binary evaluation decision threshold (use constructor Evaluation.Evaluation(double) (default if not set is argmax / 0.5) - Custom cost array, using Evaluation.Evaluation(INDArray) or Evaluation.Evaluation(List, INDArray) for multi-class Note: Care should be taken when using the Evaluation class for binary classification metrics such as F1, precision, recall, etc.
EvaluationBinary	EvaluationBinary: used for evaluating networks with binary classification outputs.
EvaluationCalibration	EvaluationCalibration is an evaluation class designed to analyze the calibration of a classifier.
ROC	ROC (Receiver Operating Characteristic) for binary classifiers.
ROC.CountsForThreshold
ROCBinary	ROC (Receiver Operating Characteristic) for multi-task binary classifiers.
ROCMultiClass	ROC (Receiver Operating Characteristic) for multi-class classifiers.

Enum Summary

Enum	Description
Evaluation.Metric
EvaluationBinary.Metric
ROC.Metric	AUROC: Area under ROC curve AUPRC: Area under Precision-Recall Curve
ROCBinary.Metric	AUROC: Area under ROC curve AUPRC: Area under Precision-Recall Curve
ROCMultiClass.Metric	AUROC: Area under ROC curve AUPRC: Area under Precision-Recall Curve