Interface ClassifierEvaluationMetrics.Builder
- All Superinterfaces:
Buildable
,CopyableBuilder<ClassifierEvaluationMetrics.Builder,
,ClassifierEvaluationMetrics> SdkBuilder<ClassifierEvaluationMetrics.Builder,
,ClassifierEvaluationMetrics> SdkPojo
- Enclosing class:
ClassifierEvaluationMetrics
-
Method Summary
Modifier and TypeMethodDescriptionThe fraction of the labels that were correct recognized.A measure of how accurate the classifier results are for the test data.hammingLoss
(Double hammingLoss) Indicates the fraction of labels that are incorrectly predicted.microF1Score
(Double microF1Score) A measure of how accurate the classifier results are for the test data.microPrecision
(Double microPrecision) A measure of the usefulness of the recognizer results in the test data.microRecall
(Double microRecall) A measure of how complete the classifier results are for the test data.A measure of the usefulness of the classifier results in the test data.A measure of how complete the classifier results are for the test data.Methods inherited from interface software.amazon.awssdk.utils.builder.CopyableBuilder
copy
Methods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilder
applyMutation, build
Methods inherited from interface software.amazon.awssdk.core.SdkPojo
equalsBySdkFields, sdkFields
-
Method Details
-
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.
- Parameters:
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.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
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.
- Parameters:
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.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
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.
- Parameters:
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.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
f1Score
A measure of how accurate the classifier results are for the test data. It is derived from the
Precision
andRecall
values. TheF1Score
is the harmonic average of the two scores. The highest score is 1, and the worst score is 0.- Parameters:
f1Score
- A measure of how accurate the classifier results are for the test data. It is derived from thePrecision
andRecall
values. TheF1Score
is the harmonic average of the two scores. The highest score is 1, and the worst score is 0.- Returns:
- Returns a reference to this object so that method calls can be chained 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.
- Parameters:
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.- Returns:
- Returns a reference to this object so that method calls can be chained 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.
- Parameters:
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.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
microF1Score
A measure of how accurate the classifier results are for the test data. It is a combination of the
Micro Precision
andMicro Recall
values. TheMicro F1Score
is the harmonic mean of the two scores. The highest score is 1, and the worst score is 0.- Parameters:
microF1Score
- A measure of how accurate the classifier results are for the test data. It is a combination of theMicro Precision
andMicro Recall
values. TheMicro F1Score
is the harmonic mean of the two scores. The highest score is 1, and the worst score is 0.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-
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.
- Parameters:
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.- Returns:
- Returns a reference to this object so that method calls can be chained together.
-