AWSComprehendClassifierEvaluationMetrics Class Reference

Inherits from AWSModel : AWSMTLModel
Declared in AWSComprehendModel.h
AWSComprehendModel.m

Overview

Describes the result metrics for the test data associated with an documentation classifier.

  accuracy

The fraction of the labels that were correct recognized. It is computed by dividing the number of labels in the test documents that were correctly recognized by the total number of labels in the test documents.

@property (nonatomic, strong) NSNumber *accuracy

Declared In

AWSComprehendModel.h

  f1Score

A measure of how accurate the classifier results are for the test data. It is derived from the Precision and Recall values. The F1Score is the harmonic average of the two scores. The highest score is 1, and the worst score is 0.

@property (nonatomic, strong) NSNumber *f1Score

Declared In

AWSComprehendModel.h

  precision

A measure of the usefulness of the classifier results in the test data. High precision means that the classifier returned substantially more relevant results than irrelevant ones.

@property (nonatomic, strong) NSNumber *precision

Declared In

AWSComprehendModel.h

  recall

A measure of how complete the classifier results are for the test data. High recall means that the classifier returned most of the relevant results.

@property (nonatomic, strong) NSNumber *recall

Declared In

AWSComprehendModel.h