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| Paws::MachineLearning::CreateMLModel(3) | User Contributed Perl Documentation | Paws::MachineLearning::CreateMLModel(3) |
NAME
Paws::MachineLearning::CreateMLModel - Arguments for method CreateMLModel on Paws::MachineLearningDESCRIPTION
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:- 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.
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:
- "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.
- "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.
- "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.
- "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 don�t 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 don�t 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::MachineLearningBUGS and CONTRIBUTIONS
The source code is located here: https://github.com/pplu/aws-sdk-perlPlease report bugs to: https://github.com/pplu/aws-sdk-perl/issues
| 2015-08-06 | perl v5.32.1 |
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