Uses of Class
org.deeplearning4j.eval.BaseEvaluation

Packages that use BaseEvaluation

Package	Description
org.deeplearning4j.eval

Uses of BaseEvaluation in org.deeplearning4j.eval

Classes in org.deeplearning4j.eval with type parameters of type BaseEvaluation

Modifier and Type	Class and Description
class	BaseEvaluation<T extends BaseEvaluation> BaseEvaluation implement common evaluation functionality (for time series, etc) for Evaluation, RegressionEvaluation, ROC, ROCMultiClass etc.

Subclasses of BaseEvaluation in org.deeplearning4j.eval

Modifier and Type	Class and Description
class	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.
class	EvaluationBinary EvaluationBinary: used for evaluating networks with binary classification outputs.
class	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)
class	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
class	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.
class	ROCBinary ROC (Receiver Operating Characteristic) for multi-task binary classifiers.
class	ROCMultiClass ROC (Receiver Operating Characteristic) for multi-class classifiers.