def get_evaluation(EvaluationId=None):
    """
    Returns an Evaluation that includes metadata as well as the current status of the Evaluation .
    See also: AWS API Documentation
    
    
    :example: response = client.get_evaluation(
        EvaluationId='string'
    )
    
    
    :type EvaluationId: string
    :param EvaluationId: [REQUIRED]
            The ID of the Evaluation to retrieve. The evaluation of each MLModel is recorded and cataloged. The ID provides the means to access the information.
            

    :rtype: dict
    :return: {
        'EvaluationId': 'string',
        'MLModelId': 'string',
        'EvaluationDataSourceId': 'string',
        'InputDataLocationS3': 'string',
        'CreatedByIamUser': 'string',
        'CreatedAt': datetime(2015, 1, 1),
        'LastUpdatedAt': datetime(2015, 1, 1),
        'Name': 'string',
        'Status': 'PENDING'|'INPROGRESS'|'FAILED'|'COMPLETED'|'DELETED',
        'PerformanceMetrics': {
            'Properties': {
                'string': 'string'
            }
        },
        'LogUri': 'string',
        'Message': 'string',
        'ComputeTime': 123,
        'FinishedAt': datetime(2015, 1, 1),
        'StartedAt': datetime(2015, 1, 1)
    }
    
    
    :returns: 
    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.
    
    """
    pass
