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Manual Reference Pages  -  PAWS::MACHINELEARNING::CREATEMLMODEL (3)

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NAME

Paws::MachineLearning::CreateMLModel - Arguments for method CreateMLModel on Paws::MachineLearning

CONTENTS

DESCRIPTION

This class represents the parameters used for calling the method CreateMLModel on the Amazon Machine Learning service. Use the attributes of this class as arguments to method CreateMLModel.

You shouln’t make instances of this class. Each attribute should be used as a named argument in the call to CreateMLModel.

As an example:



  $service_obj->CreateMLModel(Att1 => $value1, Att2 => $value2, ...);



Values for attributes that are native types (Int, String, Float, etc) can passed as-is (scalar values). Values for complex Types (objects) can be passed as a HashRef. The keys and values of the hashref will be used to instance the underlying object.

ATTRIBUTES

REQUIRED MLModelId => Str

A user-supplied ID that uniquely identifies the MLModel.

    MLModelName => Str

A user-supplied name or description of the MLModel.

REQUIRED MLModelType => Str

The category of supervised learning that this MLModel will address. Choose from the following types:
o Choose REGRESSION if the MLModel will be used to predict a numeric value.
o Choose BINARY if the MLModel result has two possible values.
o Choose MULTICLASS if the MLModel result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.

    Parameters => Paws::MachineLearning::TrainingParameters

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:
o sgd.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in 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 not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.

o sgd.l2RegularizationAmount - 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 valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.

o sgd.maxPasses - 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.
o sgd.maxMLModelSizeInBytes - 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.

    Recipe => Str

The data recipe for creating MLModel. You must specify either the recipe or its URI. If you dona\k:^XXt specify a recipe or its URI, Amazon ML creates a default.

    RecipeUri => Str

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 dona\k:^XXt specify a recipe or its URI, Amazon ML creates a default.

REQUIRED TrainingDataSourceId => Str

The DataSource that points to the training data.

SEE ALSO

This class forms part of Paws, documenting arguments for method CreateMLModel in Paws::MachineLearning

BUGS and CONTRIBUTIONS

The source code is located here: https://github.com/pplu/aws-sdk-perl

Please report bugs to: https://github.com/pplu/aws-sdk-perl/issues

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perl v5.20.3 PAWS::MACHINELEARNING::CREATEMLMODEL (3) 2015-08-06

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