com.oracle.bmc.ailanguage.model

Class NamedEntityRecognitionModelMetrics.Builder

java.lang.Object

com.oracle.bmc.ailanguage.model.NamedEntityRecognitionModelMetrics.Builder

Enclosing class:

NamedEntityRecognitionModelMetrics

public static class NamedEntityRecognitionModelMetrics.Builder
extends Object

Constructor Summary

Constructors

Constructor and Description
Builder()

Method Summary

Modifier and Type	Method and Description
NamedEntityRecognitionModelMetrics	build()
NamedEntityRecognitionModelMetrics.Builder	copy(NamedEntityRecognitionModelMetrics model)
NamedEntityRecognitionModelMetrics.Builder	macroF1(Float macroF1) F1-score, is a measure of a model’s accuracy on a dataset
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	macroRecall(Float macroRecall) Measures the model’s ability to predict actual positive classes.
NamedEntityRecognitionModelMetrics.Builder	microF1(Float microF1) F1-score, is a measure of a model’s accuracy on a dataset
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	microRecall(Float microRecall) Measures the model’s ability to predict actual positive classes.
NamedEntityRecognitionModelMetrics.Builder	weightedF1(Float weightedF1) F1-score, is a measure of a model’s accuracy on a dataset
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	weightedRecall(Float weightedRecall) Measures the model’s ability to predict actual positive classes.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

Builder

public Builder()

Method Detail

microF1

public NamedEntityRecognitionModelMetrics.Builder microF1(Float microF1)

F1-score, is a measure of a model’s accuracy on a dataset

Parameters:

microF1 - the value to set

Returns:

this builder

microPrecision

public 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)

Parameters:

microPrecision - the value to set

Returns:

this builder

microRecall

public NamedEntityRecognitionModelMetrics.Builder microRecall(Float microRecall)

Measures the model’s ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

Parameters:

microRecall - the value to set

Returns:

this builder

macroF1

public NamedEntityRecognitionModelMetrics.Builder macroF1(Float macroF1)

F1-score, is a measure of a model’s accuracy on a dataset

Parameters:

macroF1 - the value to set

Returns:

this builder

macroPrecision

public 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)

Parameters:

macroPrecision - the value to set

Returns:

this builder

macroRecall

public NamedEntityRecognitionModelMetrics.Builder macroRecall(Float macroRecall)

Measures the model’s ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

Parameters:

macroRecall - the value to set

Returns:

this builder

weightedF1

public NamedEntityRecognitionModelMetrics.Builder weightedF1(Float weightedF1)

F1-score, is a measure of a model’s accuracy on a dataset

Parameters:

weightedF1 - the value to set

Returns:

this builder

weightedPrecision

public 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)

Parameters:

weightedPrecision - the value to set

Returns:

this builder

weightedRecall

public NamedEntityRecognitionModelMetrics.Builder weightedRecall(Float weightedRecall)

Measures the model’s ability to predict actual positive classes. It is the ratio between the predicted true positives and what was actually tagged. The recall metric reveals how many of the predicted classes are correct.

Parameters:

weightedRecall - the value to set

Returns:

this builder

build

public NamedEntityRecognitionModelMetrics build()

copy

public NamedEntityRecognitionModelMetrics.Builder copy(NamedEntityRecognitionModelMetrics model)