| 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.
|