class Aws::MachineLearning::Types::CreateMLModelInput

@note When making an API call, you may pass CreateMLModelInput

data as a 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",
    }

@!attribute [rw] ml_model_id

A user-supplied ID that uniquely identifies the `MLModel`.
@return [String]

@!attribute [rw] ml_model_name

A user-supplied name or description of the `MLModel`.
@return [String]

@!attribute [rw] ml_model_type

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][1].

[1]: https://docs.aws.amazon.com/machine-learning/latest/dg
@return [String]

@!attribute [rw] 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.
@return [Hash<String,String>]

@!attribute [rw] training_data_source_id

The `DataSource` that points to the training data.
@return [String]

@!attribute [rw] 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.
@return [String]

@!attribute [rw] recipe_uri

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.
@return [String]

Constants

SENSITIVE