Interface  Description 

ClassificationMetric 
An abstract interface to measure the classification performance.

ClusteringMetric 
An abstract interface to measure the clustering performance.

CrossEntropy 
Cross entropy generalizes the log loss metric to multiclass problems.

ProbabilisticClassificationMetric 
An abstract interface to measure the probabilistic classification performance.

RegressionMetric 
An abstract interface to measure the regression performance.

Class  Description 

Accuracy 
The accuracy is the proportion of true results (both true positives and
true negatives) in the population.

AdjustedMutualInformation 
Adjusted Mutual Information (AMI) for comparing clustering.

AdjustedRandIndex 
Adjusted Rand Index.

AUC 
The area under the curve (AUC).

ConfusionMatrix 
The confusion matrix of truth and predictions.

Error 
The number of errors in the population.

Fallout 
Fallout, false alarm rate, or false positive rate (FPR)

FDR 
The false discovery rate (FDR) is ratio of false positives
to combined true and false positives, which is actually 1  precision.

FScore 
The Fscore (or Fmeasure) considers both the precision and the recall of the test
to compute the score.

LogLoss 
Log loss is a evaluation metric for binary classifiers and it is sometimes
the optimization objective as well in case of logistic regression and neural
networks.

MAD 
Mean absolute deviation error.

MatthewsCorrelation 
Matthews correlation coefficient.

MSE 
Mean squared error.

MutualInformation 
Mutual Information for comparing clustering.

NormalizedMutualInformation 
Normalized Mutual Information (NMI) for comparing clustering.

Precision 
The precision or positive predictive value (PPV) is ratio of true positives
to combined true and false positives, which is different from sensitivity.

R2 
R^{2}.

RandIndex 
Rand Index.

Recall 
In information retrieval area, sensitivity is called recall.

RMSE 
Root mean squared error.

RSS 
Residual sum of squares.

Sensitivity 
Sensitivity or true positive rate (TPR) (also called hit rate, recall) is a
statistical measures of the performance of a binary classification test.

Specificity 
Specificity (SPC) or True Negative Rate is a statistical measures of the
performance of a binary classification test.

Enum  Description 

AdjustedMutualInformation.Method 
The normalization method.

NormalizedMutualInformation.Method 
The normalization method.
