/ 
Paws-0.46
River stage two • 9 direct dependents • 11 total dependents
/ Paws::MachineLearning::MLModel

NAME

Paws::MachineLearning::MLModel

USAGE

This class represents one of two things:

Arguments in a call to a service

Use the attributes of this class as arguments to methods. You shouldn't make instances of this class. Each attribute should be used as a named argument in the calls that expect this type of object.

As an example, if Att1 is expected to be a Paws::MachineLearning::MLModel object:

$service_obj->Method(Att1 => { Algorithm => $value, ..., TrainingParameters => $value  });

Results returned from an API call

Use accessors for each attribute. If Att1 is expected to be an Paws::MachineLearning::MLModel object:

$result = $service_obj->Method(...);
$result->Att1->Algorithm

DESCRIPTION

Represents the output of a GetMLModel operation.

The content consists of the detailed metadata and the current status of the MLModel.

ATTRIBUTES

Algorithm => Str

The algorithm used to train the MLModel. The following algorithm is supported:

  • SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

ComputeTime => Int

CreatedAt => Str

The time that the MLModel was created. The time is expressed in epoch time.

CreatedByIamUser => Str

The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

EndpointInfo => Paws::MachineLearning::RealtimeEndpointInfo

The current endpoint of the MLModel.

FinishedAt => Str

InputDataLocationS3 => Str

The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

LastUpdatedAt => Str

The time of the most recent edit to the MLModel. The time is expressed in epoch time.

Message => Str

A description of the most recent details about accessing the MLModel.

MLModelId => Str

The ID assigned to the MLModel at creation.

MLModelType => Str

Identifies the MLModel category. The following are the available types:

  • REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"

  • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".

  • MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".

Name => Str

A user-supplied name or description of the MLModel.

ScoreThreshold => Num

ScoreThresholdLastUpdatedAt => Str

The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

SizeInBytes => Int

StartedAt => Str

Status => Str

The current status of an MLModel. This element can have one of the following values:

  • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.

  • INPROGRESS - The creation process is underway.

  • FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.

  • COMPLETED - The creation process completed successfully.

  • DELETED - The MLModel is marked as deleted. It isn't usable.

TrainingDataSourceId => Str

The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

TrainingParameters => 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.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.

  • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter 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 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, which 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.

SEE ALSO

This class forms part of Paws, describing an object used 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