Package com.oracle.bmc.ailanguage.model

Class TextClassificationModelMetrics.Builder

java.lang.Object

com.oracle.bmc.ailanguage.model.TextClassificationModelMetrics.Builder

Enclosing class:

TextClassificationModelMetrics

public static class TextClassificationModelMetrics.Builder
extends Object

Constructor Summary

Constructors

Constructor	Description
Builder()

Method Summary

Modifier and Type	Method	Description
TextClassificationModelMetrics.Builder	accuracy(Float accuracy)	The fraction of the labels that were correctly recognised .
TextClassificationModelMetrics	build()
TextClassificationModelMetrics.Builder	copy(TextClassificationModelMetrics model)
TextClassificationModelMetrics.Builder	macroF1(Float macroF1)	F1-score, is a measure of a model\u2019s accuracy on a dataset
TextClassificationModelMetrics.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)
TextClassificationModelMetrics.Builder	macroRecall(Float macroRecall)	Measures the model’s ability to predict actual positive classes.
TextClassificationModelMetrics.Builder	microF1(Float microF1)	F1-score, is a measure of a model\u2019s accuracy on a dataset
TextClassificationModelMetrics.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)
TextClassificationModelMetrics.Builder	microRecall(Float microRecall)	Measures the model’s ability to predict actual positive classes.
TextClassificationModelMetrics.Builder	weightedF1(Float weightedF1)	F1-score, is a measure of a model\u2019s accuracy on a dataset
TextClassificationModelMetrics.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)
TextClassificationModelMetrics.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

accuracy

public TextClassificationModelMetrics.Builder accuracy(Float accuracy)

The fraction of the labels that were correctly recognised .

Parameters:

accuracy - the value to set

Returns:

this builder

microF1

public TextClassificationModelMetrics.Builder microF1(Float microF1)

F1-score, is a measure of a model\u2019s accuracy on a dataset

Parameters:

microF1 - the value to set

Returns:

this builder

microPrecision

public TextClassificationModelMetrics.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 TextClassificationModelMetrics.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 TextClassificationModelMetrics.Builder macroF1(Float macroF1)

F1-score, is a measure of a model\u2019s accuracy on a dataset

Parameters:

macroF1 - the value to set

Returns:

this builder

macroPrecision

public TextClassificationModelMetrics.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 TextClassificationModelMetrics.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 TextClassificationModelMetrics.Builder weightedF1(Float weightedF1)

F1-score, is a measure of a model\u2019s accuracy on a dataset

Parameters:

weightedF1 - the value to set

Returns:

this builder

weightedPrecision

public TextClassificationModelMetrics.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 TextClassificationModelMetrics.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 TextClassificationModelMetrics build()

copy

public TextClassificationModelMetrics.Builder copy(TextClassificationModelMetrics model)