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Class: Aws::MachineLearning::Types::CreateMLModelInput

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Note:

When passing CreateMLModelInput as input to an Aws::Client method, you can use a vanilla Hash:

{
  ml_model_id: "EntityId", # required
  ml_model_name: "EntityName",
  ml_model_type: "REGRESSION", # required, accepts REGRESSION, BINARY, MULTICLASS
  parameters: {
    "StringType" => "StringType",
  },
  training_data_source_id: "EntityId", # required
  recipe: "Recipe",
  recipe_uri: "S3Url",
}

Instance Attribute Summary collapse

Instance Attribute Details

#ml_model_idString

A user-supplied ID that uniquely identifies the MLModel.

Returns:

  • (String)

    A user-supplied ID that uniquely identifies the MLModel.

#ml_model_nameString

A user-supplied name or description of the MLModel.

Returns:

  • (String)

    A user-supplied name or description of the MLModel.

#ml_model_typeString

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:

  • (String)

    The category of supervised learning that this MLModel will address.

#parametersHash<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 <?oxy_insert_start author=\"laurama\" timestamp=\"20160329T131121-0700\">strongly recommend that you shuffle your data.<?oxy_insert_end>

  • 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:

  • (Hash<String,String>)

    A list of the training parameters in the MLModel.

#recipeString

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:

  • (String)

    The data recipe for creating the MLModel.

#recipe_uriString

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:

  • (String)

    The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe.

#training_data_source_idString

The DataSource that points to the training data.

Returns:

  • (String)

    The DataSource that points to the training data.