package Paws::MachineLearning::CreateMLModel; use Moose; has MLModelId => (is => 'ro', isa => 'Str', required => 1); has MLModelName => (is => 'ro', isa => 'Str'); has MLModelType => (is => 'ro', isa => 'Str', required => 1); has Parameters => (is => 'ro', isa => 'Paws::MachineLearning::TrainingParameters'); has Recipe => (is => 'ro', isa => 'Str'); has RecipeUri => (is => 'ro', isa => 'Str'); has TrainingDataSourceId => (is => 'ro', isa => 'Str', required => 1); use MooseX::ClassAttribute; class_has _api_call => (isa => 'Str', is => 'ro', default => 'CreateMLModel'); class_has _returns => (isa => 'Str', is => 'ro', default => 'Paws::MachineLearning::CreateMLModelOutput'); class_has _result_key => (isa => 'Str', is => 'ro'); 1; ### main pod documentation begin ### =head1 NAME Paws::MachineLearning::CreateMLModel - Arguments for method CreateMLModel on L =head1 DESCRIPTION This class represents the parameters used for calling the method CreateMLModel on the L service. Use the attributes of this class as arguments to method CreateMLModel. You shouldn't make instances of this class. Each attribute should be used as a named argument in the call to CreateMLModel. =head1 SYNOPSIS my $machinelearning = Paws->service('MachineLearning'); my $CreateMLModelOutput = $machinelearning->CreateMLModel( MLModelId => 'MyEntityId', MLModelType => 'REGRESSION', TrainingDataSourceId => 'MyEntityId', MLModelName => 'MyEntityName', # OPTIONAL Parameters => { 'MyStringType' => 'MyStringType', }, # OPTIONAL Recipe => 'MyRecipe', # OPTIONAL RecipeUri => 'MyS3Url', # OPTIONAL ); # Results: my $MLModelId = $CreateMLModelOutput->MLModelId; # Returns a L object. 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. For the AWS API documentation, see L =head1 ATTRIBUTES =head2 B MLModelId => Str A user-supplied ID that uniquely identifies the C. =head2 MLModelName => Str A user-supplied name or description of the C. =head2 B MLModelType => Str The category of supervised learning that this C will address. Choose from the following types: =over =item * Choose C if the C will be used to predict a numeric value. =item * Choose C if the C result has two possible values. =item * Choose C if the C result has a limited number of values. =back For more information, see the Amazon Machine Learning Developer Guide (https://docs.aws.amazon.com/machine-learning/latest/dg). Valid values are: C<"REGRESSION">, C<"BINARY">, C<"MULTICLASS"> =head2 Parameters => L A list of the training parameters in the C. The list is implemented as a map of key-value pairs. The following is the current set of training parameters: =over =item * C - 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 C<100000> to C<2147483648>. The default value is C<33554432>. =item * C - The number of times that the training process traverses the observations to build the C. The value is an integer that ranges from C<1> to C<10000>. The default value is C<10>. =item * C - 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 C and C. The default value is C. We strongly recommend that you shuffle your data. =item * C - 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 C<1.0E-08>. The value is a double that ranges from C<0> to C. The default is to not use L1 normalization. This parameter can't be used when C is specified. Use this parameter sparingly. =item * C - 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 C<1.0E-08>. The value is a double that ranges from C<0> to C. The default is to not use L2 normalization. This parameter can't be used when C is specified. Use this parameter sparingly. =back =head2 Recipe => Str The data recipe for creating the C. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default. =head2 RecipeUri => Str The Amazon Simple Storage Service (Amazon S3) location and file name that contains the C 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. =head2 B TrainingDataSourceId => Str The C that points to the training data. =head1 SEE ALSO This class forms part of L, documenting arguments for method CreateMLModel in L =head1 BUGS and CONTRIBUTIONS The source code is located here: L Please report bugs to: L =cut