Uses of Package
org.nd4j.evaluation.classification

Packages that use org.nd4j.evaluation.classification

Package	Description
org.nd4j.autodiff.listeners.impl
org.nd4j.evaluation.classification
org.nd4j.evaluation.serde

Classes in org.nd4j.evaluation.classification used by org.nd4j.autodiff.listeners.impl

Class and Description
Evaluation.Metric

Classes in org.nd4j.evaluation.classification used by org.nd4j.evaluation.classification

Class and Description
ConfusionMatrix
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.
Evaluation.Metric
EvaluationBinary EvaluationBinary: used for evaluating networks with binary classification outputs.
EvaluationBinary.Metric
EvaluationCalibration EvaluationCalibration is an evaluation class designed to analyze the calibration of a classifier. It provides a number of tools for this purpose: - Counts of the number of labels and predictions for each class - Reliability diagram (or reliability curve) - Residual plot (histogram) - Histograms of probabilities, including probabilities for each class separately References: - Reliability diagram: see for example Niculescu-Mizil and Caruana 2005, Predicting Good Probabilities With Supervised Learning - Residual plot: see Wallace and Dahabreh 2012, Class Probability Estimates are Unreliable for Imbalanced Data (and How to Fix Them)
ROC ROC (Receiver Operating Characteristic) for binary classifiers. ROC has 2 modes of operation: (a) Thresholded (less memory) (b) Exact (default; use numSteps == 0 to set.
ROC.CountsForThreshold
ROC.Metric AUROC: Area under ROC curve AUPRC: Area under Precision-Recall Curve
ROCBinary ROC (Receiver Operating Characteristic) for multi-task binary classifiers.
ROCBinary.Metric AUROC: Area under ROC curve AUPRC: Area under Precision-Recall Curve
ROCMultiClass ROC (Receiver Operating Characteristic) for multi-class classifiers.
ROCMultiClass.Metric AUROC: Area under ROC curve AUPRC: Area under Precision-Recall Curve

Classes in org.nd4j.evaluation.classification used by org.nd4j.evaluation.serde

Class and Description
ConfusionMatrix
ROC ROC (Receiver Operating Characteristic) for binary classifiers. ROC has 2 modes of operation: (a) Thresholded (less memory) (b) Exact (default; use numSteps == 0 to set.