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 |
Fall-out, 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 F-score (or F-measure) 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 |
R2.
|
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.
|