java.lang.Object
- software.amazon.awssdk.core.SdkRequest
- - software.amazon.awssdk.awscore.AwsRequest
  - - software.amazon.awssdk.services.machinelearning.model.MachineLearningRequest
    - - software.amazon.awssdk.services.machinelearning.model.CreateMlModelRequest

All Implemented Interfaces:: SdkPojo, ToCopyableBuilder<CreateMlModelRequest.Builder,CreateMlModelRequest>

@Generated("software.amazon.awssdk:codegen")
public final class CreateMlModelRequest
extends MachineLearningRequest
implements ToCopyableBuilder<CreateMlModelRequest.Builder,CreateMlModelRequest>

Nested Class Summary

Nested Classes
Modifier and Type Class Description

static interface CreateMlModelRequest.Builder

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`static CreateMlModelRequest.Builder`	`builder()`
`boolean`	`equals(Object obj)`
`boolean`	`equalsBySdkFields(Object obj)`
`<T> Optional<T>`	`getValueForField(String fieldName, Class<T> clazz)`
`int`	`hashCode()`
`boolean`	`hasParameters()`	For responses, this returns true if the service returned a value for the Parameters property.
`String`	`mlModelId()`	A user-supplied ID that uniquely identifies the `MLModel`.
`String`	`mlModelName()`	A user-supplied name or description of the `MLModel`.
`MLModelType`	`mlModelType()`	The category of supervised learning that this `MLModel` will address.
`String`	`mlModelTypeAsString()`	The category of supervised learning that this `MLModel` will address.
`Map<String,String>`	`parameters()`	A list of the training parameters in the `MLModel`.
`String`	`recipe()`	The data recipe for creating the `MLModel`.
`String`	`recipeUri()`	The Amazon Simple Storage Service (Amazon S3) location and file name that contains the `MLModel` recipe.
`Map<String,SdkField<?>>`	`sdkFieldNameToField()`
`List<SdkField<?>>`	`sdkFields()`
`static Class<? extends CreateMlModelRequest.Builder>`	`serializableBuilderClass()`
`CreateMlModelRequest.Builder`	`toBuilder()`
`String`	`toString()`	Returns a string representation of this object.
`String`	`trainingDataSourceId()`	The `DataSource` that points to the training data.

Methods inherited from class software.amazon.awssdk.awscore.AwsRequest
overrideConfiguration

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
  - mlModelId
```
public final String mlModelId()
```
    A user-supplied ID that uniquely identifies the MLModel.
    
    Returns:
    
    A user-supplied ID that uniquely identifies the MLModel.
  - mlModelName
```
public final String mlModelName()
```
    A user-supplied name or description of the MLModel.
    
    Returns:
    
    A user-supplied name or description of the MLModel.
  - mlModelType
```
public final MLModelType mlModelType()
```
    The category of supervised learning that this MLModel will address. Choose from the following types:
    
    Choose REGRESSION if the MLModel will be used to predict a numeric value.
    
    Choose BINARY if the MLModel result has two possible values.
    
    Choose MULTICLASS if the MLModel result has a limited number of values.
    
    For more information, see the Amazon Machine Learning Developer Guide.
    
    If the service returns an enum value that is not available in the current SDK version, mlModelType will return MLModelType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from mlModelTypeAsString().
    Returns:
    
    The category of supervised learning that this MLModel will address. Choose from the following types:
    
    Choose REGRESSION if the MLModel will be used to predict a numeric value.
    
    Choose BINARY if the MLModel result has two possible values.
    
    Choose MULTICLASS if the MLModel result has a limited number of values.
    
    For more information, see the Amazon Machine Learning Developer Guide.
    
    See Also:
    
    MLModelType
  - mlModelTypeAsString
```
public final String mlModelTypeAsString()
```
    The category of supervised learning that this MLModel will address. Choose from the following types:
    
    Choose REGRESSION if the MLModel will be used to predict a numeric value.
    
    Choose BINARY if the MLModel result has two possible values.
    
    Choose MULTICLASS if the MLModel result has a limited number of values.
    
    For more information, see the Amazon Machine Learning Developer Guide.
    
    If the service returns an enum value that is not available in the current SDK version, mlModelType will return MLModelType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from mlModelTypeAsString().
    Returns:
    
    The category of supervised learning that this MLModel will address. Choose from the following types:
    
    Choose REGRESSION if the MLModel will be used to predict a numeric value.
    
    Choose BINARY if the MLModel result has two possible values.
    
    Choose MULTICLASS if the MLModel result has a limited number of values.
    
    For more information, see the Amazon Machine Learning Developer Guide.
    
    See Also:
    
    MLModelType
  - hasParameters
```
public final boolean hasParameters()
```
    For responses, this returns true if the service returned a value for the Parameters property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.
  - parameters
```
public final Map<String,String> parameters()
```
    A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.
    
    The following is the current set of training parameters:
    
    sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.
    
    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
    
    sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.
    
    sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. We strongly recommend that you shuffle your data.
    
    sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.
    
    The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.
    
    sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.
    
    The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.
    
    Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.
    
    This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasParameters() method.
    Returns:
    
    A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.
    
    The following is the current set of training parameters:
    
    sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.
    
    The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
    
    sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.
    
    sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. We strongly recommend that you shuffle your data.
    
    sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.
    
    The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.
    
    sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.
    
    The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.
  - trainingDataSourceId
```
public final String trainingDataSourceId()
```
    The DataSource that points to the training data.
    
    Returns:
    
    The DataSource that points to the training data.
  - recipe
```
public final String recipe()
```
    The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.
    
    Returns:
    
    The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.
  - recipeUri
```
public final String recipeUri()
```
    The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.
    
    Returns:
    
    The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.
  - toBuilder
```
public CreateMlModelRequest.Builder toBuilder()
```
    Specified by:
    
    toBuilder in interface ToCopyableBuilder<CreateMlModelRequest.Builder,CreateMlModelRequest>
    
    Specified by:
    
    toBuilder in class MachineLearningRequest
  - builder
```
public static CreateMlModelRequest.Builder builder()
```
  - serializableBuilderClass
```
public static Class<? extends CreateMlModelRequest.Builder> serializableBuilderClass()
```
  - hashCode
```
public final int hashCode()
```
    Overrides:
    
    hashCode in class AwsRequest
  - equals
```
public final boolean equals(Object obj)
```
    Overrides:
    
    equals in class AwsRequest
  - equalsBySdkFields
```
public final boolean equalsBySdkFields(Object obj)
```
    Specified by:
    
    equalsBySdkFields in interface SdkPojo
  - 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.
    
    Overrides:
    
    toString in class Object
  - getValueForField
```
public final <T> Optional<T> getValueForField(String fieldName,
                                              Class<T> clazz)
```
    Overrides:
    
    getValueForField in class SdkRequest
  - sdkFields
```
public final List<SdkField<?>> sdkFields()
```
    Specified by:
    
    sdkFields in interface SdkPojo
  - sdkFieldNameToField
```
public final Map<String,SdkField<?>> sdkFieldNameToField()
```
    Specified by:
    
    sdkFieldNameToField in interface SdkPojo

Class CreateMlModelRequest

Nested Class Summary

Method Summary

Methods inherited from class software.amazon.awssdk.awscore.AwsRequest

Methods inherited from class java.lang.Object

Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder

Method Detail

mlModelId

mlModelName

mlModelType

mlModelTypeAsString

hasParameters

parameters

trainingDataSourceId

recipe

recipeUri

toBuilder

builder

serializableBuilderClass

hashCode

equals

equalsBySdkFields

toString

getValueForField

sdkFields

sdkFieldNameToField