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
0EvaluatorAn 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.