Class ClassifierEvaluationMetrics
- java.lang.Object
-
- software.amazon.awssdk.services.comprehend.model.ClassifierEvaluationMetrics
-
- All Implemented Interfaces:
Serializable,SdkPojo,ToCopyableBuilder<ClassifierEvaluationMetrics.Builder,ClassifierEvaluationMetrics>
@Generated("software.amazon.awssdk:codegen") public final class ClassifierEvaluationMetrics extends Object implements SdkPojo, Serializable, ToCopyableBuilder<ClassifierEvaluationMetrics.Builder,ClassifierEvaluationMetrics>
Describes the result metrics for the test data associated with an documentation classifier.
- See Also:
- Serialized Form
-
-
Nested Class Summary
Nested Classes Modifier and Type Class Description static interfaceClassifierEvaluationMetrics.Builder
-
Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description Doubleaccuracy()The fraction of the labels that were correct recognized.static ClassifierEvaluationMetrics.Builderbuilder()booleanequals(Object obj)booleanequalsBySdkFields(Object obj)Doublef1Score()A measure of how accurate the classifier results are for the test data.<T> Optional<T>getValueForField(String fieldName, Class<T> clazz)DoublehammingLoss()Indicates the fraction of labels that are incorrectly predicted.inthashCode()DoublemicroF1Score()A measure of how accurate the classifier results are for the test data.DoublemicroPrecision()A measure of the usefulness of the recognizer results in the test data.DoublemicroRecall()A measure of how complete the classifier results are for the test data.Doubleprecision()A measure of the usefulness of the classifier results in the test data.Doublerecall()A measure of how complete the classifier results are for the test data.List<SdkField<?>>sdkFields()static Class<? extends ClassifierEvaluationMetrics.Builder>serializableBuilderClass()ClassifierEvaluationMetrics.BuildertoBuilder()StringtoString()Returns a string representation of this object.-
Methods inherited from class java.lang.Object
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
-
Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder
copy
-
-
-
-
Method Detail
-
accuracy
public final 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.
- Returns:
- 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.
-
precision
public final 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.
- Returns:
- 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.
-
recall
public final 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.
- Returns:
- 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.
-
f1Score
public final Double f1Score()
A measure of how accurate the classifier results are for the test data. It is derived from the
PrecisionandRecallvalues. TheF1Scoreis the harmonic average of the two scores. The highest score is 1, and the worst score is 0.- Returns:
- A measure of how accurate the classifier results are for the test data. It is derived from the
PrecisionandRecallvalues. TheF1Scoreis the harmonic average of the two scores. The highest score is 1, and the worst score is 0.
-
microPrecision
public final 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.
- Returns:
- 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.
-
microRecall
public final 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.
- Returns:
- 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.
-
microF1Score
public final Double microF1Score()
A measure of how accurate the classifier results are for the test data. It is a combination of the
Micro PrecisionandMicro Recallvalues. TheMicro F1Scoreis the harmonic mean of the two scores. The highest score is 1, and the worst score is 0.- Returns:
- A measure of how accurate the classifier results are for the test data. It is a combination of the
Micro PrecisionandMicro Recallvalues. TheMicro F1Scoreis the harmonic mean of the two scores. The highest score is 1, and the worst score is 0.
-
hammingLoss
public final 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.
- Returns:
- 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.
-
toBuilder
public ClassifierEvaluationMetrics.Builder toBuilder()
- Specified by:
toBuilderin interfaceToCopyableBuilder<ClassifierEvaluationMetrics.Builder,ClassifierEvaluationMetrics>
-
builder
public static ClassifierEvaluationMetrics.Builder builder()
-
serializableBuilderClass
public static Class<? extends ClassifierEvaluationMetrics.Builder> serializableBuilderClass()
-
equalsBySdkFields
public final boolean equalsBySdkFields(Object obj)
- Specified by:
equalsBySdkFieldsin interfaceSdkPojo
-
toString
public final String toString()
Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
-
-