BinaryClassificationMetrics Class

Definition

Namespace:

Microsoft.ML.Data

Assembly:

Microsoft.ML.Data.dll

Package:

Microsoft.ML v3.0.1

Evaluation results for binary classifiers, excluding probabilistic metrics.

public class BinaryClassificationMetrics

type BinaryClassificationMetrics = class

Public Class BinaryClassificationMetrics

Inheritance

Object

BinaryClassificationMetrics

Derived

Microsoft.ML.Data.CalibratedBinaryClassificationMetrics

Properties

Accuracy	Gets the accuracy of a classifier which is the proportion of correct predictions in the test set.
AreaUnderPrecisionRecallCurve	Gets the area under the precision/recall curve of the classifier.
AreaUnderRocCurve	Gets the area under the ROC curve.
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	Gets the F1 score of the classifier, which is a measure of the classifier's quality considering both precision and recall.
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	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	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	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).