| Class | Description |
|---|---|
| BinaryClassificationEvaluator |
Evaluator for binary classification, which expects input columns rawPrediction, label and
an optional weight column.
|
| ClusteringEvaluator |
Evaluator for clustering results.
|
| ClusteringMetrics |
Metrics for clustering, which expects two input columns: prediction and label.
|
| CosineSilhouette |
The algorithm which is implemented in this object, instead, is an efficient and parallel
implementation of the Silhouette using the cosine distance measure.
|
| Evaluator |
Abstract class for evaluators that compute metrics from predictions.
|
| MulticlassClassificationEvaluator |
Evaluator for multiclass classification, which expects input columns: prediction, label,
weight (optional) and probability (only for logLoss).
|
| MultilabelClassificationEvaluator |
:: Experimental ::
Evaluator for multi-label classification, which expects two input
columns: prediction and label.
|
| RankingEvaluator |
:: Experimental ::
Evaluator for ranking, which expects two input columns: prediction and label.
|
| RegressionEvaluator |
Evaluator for regression, which expects input columns prediction, label and
an optional weight column.
|
| SquaredEuclideanSilhouette |
SquaredEuclideanSilhouette computes the average of the
Silhouette over all the data of the dataset, which is
a measure of how appropriately the data have been clustered.
|
| SquaredEuclideanSilhouette.ClusterStats | |
| SquaredEuclideanSilhouette.ClusterStats$ |