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CreateMLModelInput

import "github.com/aws/aws-sdk-go/service/machinelearning"

type CreateMLModelInput struct { MLModelId *string `min:"1" type:"string" required:"true"` MLModelName *string `type:"string"` MLModelType *string `type:"string" required:"true" enum:"MLModelType"` Parameters map[string]*string `type:"map"` Recipe *string `type:"string"` RecipeUri *string `type:"string"` TrainingDataSourceId *string `min:"1" type:"string" required:"true"` }

MLModelId

Type: *string

A user-supplied ID that uniquely identifies the MLModel.

MLModelId is a required field

MLModelName

Type: *string

A user-supplied name or description of the MLModel.

MLModelType

Type: *string

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 (https://docs.aws.amazon.com/machine-learning/latest/dg).

MLModelType is a required field

Parameters

Type: map[string]*string

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.
Recipe

Type: *string

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

Type: *string

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.

TrainingDataSourceId

Type: *string

The DataSource that points to the training data.

TrainingDataSourceId is a required field

Method

GoString

func (s CreateMLModelInput) GoString() string

GoString returns the string representation

SetMLModelId

func (s *CreateMLModelInput) SetMLModelId(v string) *CreateMLModelInput

SetMLModelId sets the MLModelId field's value.

SetMLModelName

func (s *CreateMLModelInput) SetMLModelName(v string) *CreateMLModelInput

SetMLModelName sets the MLModelName field's value.

SetMLModelType

func (s *CreateMLModelInput) SetMLModelType(v string) *CreateMLModelInput

SetMLModelType sets the MLModelType field's value.

SetParameters

func (s *CreateMLModelInput) SetParameters(v map[string]*string) *CreateMLModelInput

SetParameters sets the Parameters field's value.

SetRecipe

func (s *CreateMLModelInput) SetRecipe(v string) *CreateMLModelInput

SetRecipe sets the Recipe field's value.

SetRecipeUri

func (s *CreateMLModelInput) SetRecipeUri(v string) *CreateMLModelInput

SetRecipeUri sets the RecipeUri field's value.

SetTrainingDataSourceId

func (s *CreateMLModelInput) SetTrainingDataSourceId(v string) *CreateMLModelInput

SetTrainingDataSourceId sets the TrainingDataSourceId field's value.

String

func (s CreateMLModelInput) String() string

String returns the string representation

Validate

func (s *CreateMLModelInput) Validate() error

Validate inspects the fields of the type to determine if they are valid.

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