# BinaryClassificationMetricsBinaryClassificationMetricsBinaryClassificationMetrics Class

## Definition

Evaluation results for binary classifiers, excluding probabilistic metrics.

public class BinaryClassificationMetrics
type BinaryClassificationMetrics = class
Public Class BinaryClassificationMetrics
Inheritance
BinaryClassificationMetricsBinaryClassificationMetricsBinaryClassificationMetrics
Derived

## Properties

 Accuracy Accuracy Accuracy Gets the accuracy of a classifier which is the proportion of correct predictions in the test set. AreaUnderPrecisionRecallCurve AreaUnderPrecisionRecallCurve AreaUnderPrecisionRecallCurve Gets the area under the precision/recall curve of the classifier. AreaUnderRocCurve AreaUnderRocCurve AreaUnderRocCurve Gets the area under the ROC curve. ConfusionMatrix ConfusionMatrix ConfusionMatrix The confusion matrix giving the counts of the true positives, true negatives, false positives and false negatives for the two classes of data. F1Score F1Score F1Score Gets the F1 score of the classifier. NegativePrecision NegativePrecision NegativePrecision Gets the negative precision of a classifier which is the proportion of correctly predicted negative instances among all the negative predictions (i.e., the number of negative instances predicted as negative, divided by the total number of instances predicted as negative). NegativeRecall NegativeRecall NegativeRecall Gets the negative recall of a classifier which is the proportion of correctly predicted negative instances among all the negative instances (i.e., the number of negative instances predicted as negative, divided by the total number of negative instances). PositivePrecision PositivePrecision PositivePrecision Gets the positive precision of a classifier which is the proportion of correctly predicted positive instances among all the positive predictions (i.e., the number of positive instances predicted as positive, divided by the total number of instances predicted as positive). PositiveRecall PositiveRecall PositiveRecall Gets the positive recall of a classifier which is the proportion of correctly predicted positive instances among all the positive instances (i.e., the number of positive instances predicted as positive, divided by the total number of positive instances).