public static interface ClassifierEvaluationMetrics.Builder extends SdkPojo, CopyableBuilder<ClassifierEvaluationMetrics.Builder,ClassifierEvaluationMetrics>
Modifier and Type  Method and Description 

ClassifierEvaluationMetrics.Builder 
accuracy(Double accuracy)
The fraction of the labels that were correct recognized.

ClassifierEvaluationMetrics.Builder 
f1Score(Double f1Score)
A measure of how accurate the classifier results are for the test data.

ClassifierEvaluationMetrics.Builder 
hammingLoss(Double hammingLoss)
Indicates the fraction of labels that are incorrectly predicted.

ClassifierEvaluationMetrics.Builder 
microF1Score(Double microF1Score)
A measure of how accurate the classifier results are for the test data.

ClassifierEvaluationMetrics.Builder 
microPrecision(Double microPrecision)
A measure of the usefulness of the recognizer results in the test data.

ClassifierEvaluationMetrics.Builder 
microRecall(Double microRecall)
A measure of how complete the classifier results are for the test data.

ClassifierEvaluationMetrics.Builder 
precision(Double precision)
A measure of the usefulness of the classifier results in the test data.

ClassifierEvaluationMetrics.Builder 
recall(Double recall)
A measure of how complete the classifier results are for the test data.

equalsBySdkFields, sdkFields
copy
applyMutation, build
ClassifierEvaluationMetrics.Builder accuracy(Double 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.
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.ClassifierEvaluationMetrics.Builder precision(Double 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.
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.ClassifierEvaluationMetrics.Builder recall(Double 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.
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.ClassifierEvaluationMetrics.Builder f1Score(Double 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.
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.ClassifierEvaluationMetrics.Builder microPrecision(Double microPrecision)
A measure of the usefulness of the recognizer results in the test data. High precision means that the recognizer returned substantially more relevant results than irrelevant ones. Unlike the Precision metric which comes from averaging the precision of all available labels, this is based on the overall score of all precision scores added together.
microPrecision
 A measure of the usefulness of the recognizer results in the test data. High precision means that the
recognizer returned substantially more relevant results than irrelevant ones. Unlike the Precision
metric which comes from averaging the precision of all available labels, this is based on the overall
score of all precision scores added together.ClassifierEvaluationMetrics.Builder microRecall(Double microRecall)
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. Specifically, this indicates how many of the correct categories in the text that the model can predict. It is a percentage of correct categories in the text that can found. Instead of averaging the recall scores of all labels (as with Recall), micro Recall is based on the overall score of all recall scores added together.
microRecall
 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. Specifically, this indicates how many of the correct
categories in the text that the model can predict. It is a percentage of correct categories in the
text that can found. Instead of averaging the recall scores of all labels (as with Recall), micro
Recall is based on the overall score of all recall scores added together.ClassifierEvaluationMetrics.Builder microF1Score(Double microF1Score)
A measure of how accurate the classifier results are for the test data. It is a combination of the
Micro Precision
and Micro Recall
values. The Micro F1Score
is the
harmonic mean of the two scores. The highest score is 1, and the worst score is 0.
microF1Score
 A measure of how accurate the classifier results are for the test data. It is a combination of the
Micro Precision
and Micro Recall
values. The Micro F1Score
is
the harmonic mean of the two scores. The highest score is 1, and the worst score is 0.ClassifierEvaluationMetrics.Builder hammingLoss(Double hammingLoss)
Indicates the fraction of labels that are incorrectly predicted. Also seen as the fraction of wrong labels compared to the total number of labels. Scores closer to zero are better.
hammingLoss
 Indicates the fraction of labels that are incorrectly predicted. Also seen as the fraction of wrong
labels compared to the total number of labels. Scores closer to zero are better.