Uses of Class
com.oracle.bmc.ailanguage.model.NamedEntityRecognitionModelMetrics.Builder

Packages that use NamedEntityRecognitionModelMetrics.Builder

Package	Description
com.oracle.bmc.ailanguage.model

Uses of NamedEntityRecognitionModelMetrics.Builder in com.oracle.bmc.ailanguage.model

Methods in com.oracle.bmc.ailanguage.model that return NamedEntityRecognitionModelMetrics.Builder

Modifier and Type	Method and Description
static NamedEntityRecognitionModelMetrics.Builder	NamedEntityRecognitionModelMetrics.builder() Create a new builder.
NamedEntityRecognitionModelMetrics.Builder	NamedEntityRecognitionModelMetrics.Builder.copy(NamedEntityRecognitionModelMetrics model)
NamedEntityRecognitionModelMetrics.Builder	NamedEntityRecognitionModelMetrics.Builder.macroF1(Float macroF1) F1-score, is a measure of a model’s accuracy on a dataset
NamedEntityRecognitionModelMetrics.Builder	NamedEntityRecognitionModelMetrics.Builder.macroPrecision(Float macroPrecision) Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
NamedEntityRecognitionModelMetrics.Builder	NamedEntityRecognitionModelMetrics.Builder.macroRecall(Float macroRecall) Measures the model’s ability to predict actual positive classes.
NamedEntityRecognitionModelMetrics.Builder	NamedEntityRecognitionModelMetrics.Builder.microF1(Float microF1) F1-score, is a measure of a model’s accuracy on a dataset
NamedEntityRecognitionModelMetrics.Builder	NamedEntityRecognitionModelMetrics.Builder.microPrecision(Float microPrecision) Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
NamedEntityRecognitionModelMetrics.Builder	NamedEntityRecognitionModelMetrics.Builder.microRecall(Float microRecall) Measures the model’s ability to predict actual positive classes.
NamedEntityRecognitionModelMetrics.Builder	NamedEntityRecognitionModelMetrics.toBuilder()
NamedEntityRecognitionModelMetrics.Builder	NamedEntityRecognitionModelMetrics.Builder.weightedF1(Float weightedF1) F1-score, is a measure of a model’s accuracy on a dataset
NamedEntityRecognitionModelMetrics.Builder	NamedEntityRecognitionModelMetrics.Builder.weightedPrecision(Float weightedPrecision) Precision refers to the number of true positives divided by the total number of positive predictions (i.e., the number of true positives plus the number of false positives)
NamedEntityRecognitionModelMetrics.Builder	NamedEntityRecognitionModelMetrics.Builder.weightedRecall(Float weightedRecall) Measures the model’s ability to predict actual positive classes.