com.ibm.watson.discovery.v2.model

Class CreateDocumentClassifierModelOptions

java.lang.Object

com.ibm.cloud.sdk.core.service.model.GenericModel

com.ibm.watson.discovery.v2.model.CreateDocumentClassifierModelOptions

All Implemented Interfaces:

com.ibm.cloud.sdk.core.service.model.ObjectModel

public class CreateDocumentClassifierModelOptions
extends com.ibm.cloud.sdk.core.service.model.GenericModel

The createDocumentClassifierModel options.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	CreateDocumentClassifierModelOptions.Builder Builder.

Method Summary

Modifier and Type	Method and Description
String	classifierId() Gets the classifierId.
String	description() Gets the description.
Double	improvementRatio() Gets the improvementRatio.
List<Double>	l1RegularizationStrengths() Gets the l1RegularizationStrengths.
List<Double>	l2RegularizationStrengths() Gets the l2RegularizationStrengths.
Double	learningRate() Gets the learningRate.
String	name() Gets the name.
CreateDocumentClassifierModelOptions.Builder	newBuilder() New builder.
String	projectId() Gets the projectId.
Long	trainingMaxSteps() Gets the trainingMaxSteps.

Methods inherited from class com.ibm.cloud.sdk.core.service.model.GenericModel

equals, hashCode, toString

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Method Detail

newBuilder

public CreateDocumentClassifierModelOptions.Builder newBuilder()

New builder.

Returns:

a CreateDocumentClassifierModelOptions builder

projectId

public String projectId()

Gets the projectId.

The ID of the project. This information can be found from the *Integrate and Deploy* page in Discovery.

Returns:

the projectId

classifierId

public String classifierId()

Gets the classifierId.

The ID of the classifier.

Returns:

the classifierId

name

public String name()

Gets the name.

The name of the document classifier model.

Returns:

the name

description

public String description()

Gets the description.

A description of the document classifier model.

Returns:

the description

learningRate

public Double learningRate()

Gets the learningRate.

A tuning parameter in an optimization algorithm that determines the step size at each iteration of the training process. It influences how much of any newly acquired information overrides the existing information, and therefore is said to represent the speed at which a machine learning model learns. The default value is `0.1`.

Returns:

the learningRate

l1RegularizationStrengths

public List<Double> l1RegularizationStrengths()

Gets the l1RegularizationStrengths.

Avoids overfitting by shrinking the coefficient of less important features to zero, which removes some features altogether. You can specify many values for hyper-parameter optimization. The default value is `[0.000001]`.

Returns:

the l1RegularizationStrengths

l2RegularizationStrengths

public List<Double> l2RegularizationStrengths()

Gets the l2RegularizationStrengths.

A method you can apply to avoid overfitting your model on the training data. You can specify many values for hyper-parameter optimization. The default value is `[0.000001]`.

Returns:

the l2RegularizationStrengths

trainingMaxSteps

public Long trainingMaxSteps()

Gets the trainingMaxSteps.

Maximum number of training steps to complete. This setting is useful if you need the training process to finish in a specific time frame to fit into an automated process. The default value is ten million.

Returns:

the trainingMaxSteps

improvementRatio

public Double improvementRatio()

Gets the improvementRatio.

Stops the training run early if the improvement ratio is not met by the time the process reaches a certain point. The default value is `0.00001`.

Returns:

the improvementRatio