Interface  Description 

IEvaluation<T extends IEvaluation> 
A general purpose interface for evaluating neural networks  methods are shared by implemetations such as
Evaluation , RegressionEvaluation , ROC , ROCMultiClass 
Class  Description 

BaseEvaluation<T extends BaseEvaluation> 
BaseEvaluation implement common evaluation functionality (for time series, etc) for
Evaluation ,
RegressionEvaluation , ROC , ROCMultiClass etc. 
ConfusionMatrix<T extends java.lang.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 multiclass 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.
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 NiculescuMizil 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) 
EvaluationUtils 
Utility methods for performing evaluation

RegressionEvaluation 
Evaluation method for the evaluation of regression algorithms.
Provides the following metrics, for each column:  MSE: mean squared error  MAE: mean absolute error  RMSE: root mean squared error  RSE: relative squared error  PC: pearson correlation coefficient  R^2: coefficient of determination See for example: http://www.saedsayad.com/model_evaluation_r.htm For classification, see Evaluation 
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  
ROCBinary 
ROC (Receiver Operating Characteristic) for multitask binary classifiers.

ROCMultiClass 
ROC (Receiver Operating Characteristic) for multiclass classifiers.

Enum  Description 

Evaluation.Metric  
EvaluationAveraging 
The averaging approach for binary valuation measures when applied to multiclass classification problems.

RegressionEvaluation.Metric 