CreateMLModelRequest (AWS SDK for Java

java.lang.Object
- software.amazon.awssdk.AmazonWebServiceRequest
- - software.amazon.awssdk.services.machinelearning.model.CreateMLModelRequest

All Implemented Interfaces:: Cloneable, ReadLimitInfo, ToCopyableBuilder<CreateMLModelRequest.Builder,CreateMLModelRequest>

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

Nested Class Summary

Nested Classes
Modifier and Type Class Description

static interface CreateMLModelRequest.Builder

Nested Classes
Modifier and Type	Class	Description
`static interface`	`CreateMLModelRequest.Builder`

Field Summary
- Fields inherited from class software.amazon.awssdk.AmazonWebServiceRequest
  NOOP

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`static CreateMLModelRequest.Builder`	`builder()`
`boolean`	`equals(Object obj)`
`int`	`hashCode()`
`String`	`mlModelId()`	A user-supplied ID that uniquely identifies the `MLModel`.
`String`	`mlModelName()`	A user-supplied name or description of the `MLModel`.
`String`	`mlModelType()`	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.
`static Class<? extends CreateMLModelRequest.Builder>`	`serializableBuilderClass()`
`CreateMLModelRequest.Builder`	`toBuilder()`	Take this object and create a builder that contains all of the current property values of this object.
`String`	`toString()`
`String`	`trainingDataSourceId()`	The `DataSource` that points to the training data.

Methods inherited from class java.lang.Object
getClass, notify, notifyAll, wait, wait, wait

- Method Detail
  - mlModelId
```
public String mlModelId()
```
    A user-supplied ID that uniquely identifies the MLModel.
    
    Returns:
    
    A user-supplied ID that uniquely identifies the MLModel.
  - mlModelName
```
public String mlModelName()
```
    A user-supplied name or description of the MLModel.
    
    Returns:
    
    A user-supplied name or description of the MLModel.
  - mlModelType
```
public String 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.
    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
  - parameters
```
public 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.
    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 String trainingDataSourceId()
```
    The DataSource that points to the training data.
    
    Returns:
    
    The DataSource that points to the training data.
  - recipe
```
public 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 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()
```
    Description copied from interface: ToCopyableBuilder
    
    Take this object and create a builder that contains all of the current property values of this object.
    
    Specified by:
    
    toBuilder in interface ToCopyableBuilder<CreateMLModelRequest.Builder,CreateMLModelRequest>
    
    Returns:
    
    a builder for type T
  - builder
```
public static CreateMLModelRequest.Builder builder()
```
  - serializableBuilderClass
```
public static Class<? extends CreateMLModelRequest.Builder> serializableBuilderClass()
```
  - hashCode
```
public int hashCode()
```
    Overrides:
    
    hashCode in class Object
  - equals
```
public boolean equals(Object obj)
```
    Overrides:
    
    equals in class Object
  - toString
```
public String toString()
```
    Overrides:
    
    toString in class Object

Class CreateMLModelRequest

Nested Class Summary

Field Summary

Fields inherited from class software.amazon.awssdk.AmazonWebServiceRequest

Method Summary

Methods inherited from class software.amazon.awssdk.AmazonWebServiceRequest

Methods inherited from class java.lang.Object

Method Detail

mlModelId

mlModelName

mlModelType

parameters

trainingDataSourceId

recipe

recipeUri

toBuilder

builder

serializableBuilderClass

hashCode

equals

toString