NAME

Paws::MachineLearning::Evaluation

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::Evaluation object:

  $service_obj->Method(Att1 => { ComputeTime => $value, ..., Status => $value  });

Results returned from an API call

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

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

DESCRIPTION

Represents the output of "GetEvaluation" operation.

The content consists of the detailed metadata and data file information and the current status of the "Evaluation".

ATTRIBUTES

ComputeTime => Int

CreatedAt => Str

The time that the "Evaluation" was created. The time is expressed in epoch time.

CreatedByIamUser => Str

The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

EvaluationDataSourceId => Str

The ID of the "DataSource" that is used to evaluate the "MLModel".

EvaluationId => Str

The ID that is assigned to the "Evaluation" at creation.

FinishedAt => Str

InputDataLocationS3 => Str

The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.

LastUpdatedAt => Str

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

Message => Str

A description of the most recent details about evaluating the "MLModel".

MLModelId => Str

The ID of the "MLModel" that is the focus of the evaluation.

Name => Str

A user-supplied name or description of the "Evaluation".

PerformanceMetrics => Paws::MachineLearning::PerformanceMetrics

Measurements of how well the "MLModel" performed, using observations referenced by the "DataSource". One of the following metrics is returned, based on the type of the "MLModel":

BinaryAUC: A binary "MLModel" uses the Area Under the Curve (AUC) technique to measure performance.
RegressionRMSE: A regression "MLModel" uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.
MulticlassAvgFScore: A multiclass "MLModel" uses the F1 score technique to measure performance.

For more information about performance metrics, please see the Amazon Machine Learning Developer Guide (https://docs.aws.amazon.com/machine-learning/latest/dg).

StartedAt => Str

Status => Str

The status of the evaluation. This element can have one of the following values:

"PENDING" - Amazon Machine Learning (Amazon ML) submitted a request to evaluate an "MLModel".
"INPROGRESS" - The evaluation is underway.
"FAILED" - The request to evaluate an "MLModel" did not run to completion. It is not usable.
"COMPLETED" - The evaluation process completed successfully.
"DELETED" - The "Evaluation" is marked as deleted. It is not usable.

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>