Binary Classification Evaluator

Creates a binary classification evaluator.

Available Metrics

`Area under ROC`

Receiver operating characteristic (ROC), or ROC curve, is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. For more details, view Wikipedia article.

`Area under PR`

Typically, Precision and Recall are inversely related, but when a balance between these two metrics needs to be achieved, the area under precision-recall curve could be used. For more details, view Wikipedia article.

`Precision`

Precision (also called positive predictive value) is the fraction of retrieved instances that are relevant. For more details, view Wikipedia article.

`Recall`

Recall (also known as sensitivity) is the fraction of relevant instances that are retrieved. For more details, view Wikipedia article.

`F1 Score`

F1 score (also F-score or F-measure) is a measure of a test’s accuracy. It considers both precision and recall of the test to compute the score. For more details, view Wikipedia article.

Since: Seahorse 1.0.0

Input

This operation does not take any input.

Output

Port	Type Qualifier	Description
`0`	`Evaluator`	An `Evaluator` that can be used in an Evaluate operation.

Parameters

Name	Type	Description
`binary metric`	`SingleChoice`	The metric used in evaluation. Possible values: `["Area under ROC", "Area under PR", "Precision", "Recall", "F1 Score"]`
`raw prediction column`	`SingleColumnSelector`	Valid only if `binary metric = Area under ROC` or `binary metric = Area under PR`. The raw prediction (confidence) column.
`prediction column`	`SingleColumnSelector`	Valid only if `binary metric = Precision`, `binary metric = Recall` or `binary metric = F1 Score`. The prediction column created during model scoring.
`label column`	`SingleColumnSelector`	The label column for model fitting.