|IEvaluation<T extends IEvaluation>|
|BaseEvaluation<T extends BaseEvaluation>|
|ConfusionMatrix<T extends java.lang.Comparable<? super T>>|
- precision, recall, f1, fBeta, accuracy, Matthews correlation coefficient, gMeasure
- Top N accuracy (if using constructor
- Custom binary evaluation decision threshold (use constructor
- Custom cost array, using
Note: Care should be taken when using the Evaluation class for binary classification metrics such as F1, precision, recall, etc.
EvaluationBinary: used for evaluating networks with binary classification outputs.
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
- 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)
Utility methods for performing evaluation
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
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 (Receiver Operating Characteristic) for multi-task binary classifiers.
ROC (Receiver Operating Characteristic) for multi-class classifiers.
The averaging approach for binary valuation measures when applied to multiclass classification problems.