[ aws . forecast ]

get-accuracy-metrics

Description

Provides metrics on the accuracy of the models that were trained by the CreatePredictor operation. Use metrics to see how well the model performed and to decide whether to use the predictor to generate a forecast. For more information, see Predictor Metrics .

This operation generates metrics for each backtest window that was evaluated. The number of backtest windows (NumberOfBacktestWindows ) is specified using the EvaluationParameters object, which is optionally included in the CreatePredictor request. If NumberOfBacktestWindows isn’t specified, the number defaults to one.

The parameters of the filling method determine which items contribute to the metrics. If you want all items to contribute, specify zero . If you want only those items that have complete data in the range being evaluated to contribute, specify nan . For more information, see FeaturizationMethod .

Note

Before you can get accuracy metrics, the Status of the predictor must be ACTIVE , signifying that training has completed. To get the status, use the DescribePredictor operation.

See also: AWS API Documentation

See ‘aws help’ for descriptions of global parameters.

Synopsis

  get-accuracy-metrics
--predictor-arn <value>
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]

Options

--predictor-arn (string)

The Amazon Resource Name (ARN) of the predictor to get metrics for.

--cli-input-json | --cli-input-yaml (string) Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.

--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.

See ‘aws help’ for descriptions of global parameters.

Output

PredictorEvaluationResults -> (list)

An array of results from evaluating the predictor.

(structure)

The results of evaluating an algorithm. Returned as part of the GetAccuracyMetrics response.

AlgorithmArn -> (string)

The Amazon Resource Name (ARN) of the algorithm that was evaluated.

TestWindows -> (list)

The array of test windows used for evaluating the algorithm. The NumberOfBacktestWindows from the EvaluationParameters object determines the number of windows in the array.

(structure)

The metrics for a time range within the evaluation portion of a dataset. This object is part of the EvaluationResult object.

The TestWindowStart and TestWindowEnd parameters are determined by the BackTestWindowOffset parameter of the EvaluationParameters object.

TestWindowStart -> (timestamp)

The timestamp that defines the start of the window.

TestWindowEnd -> (timestamp)

The timestamp that defines the end of the window.

ItemCount -> (integer)

The number of data points within the window.

EvaluationType -> (string)

The type of evaluation.

• SUMMARY - The average metrics across all windows.

• COMPUTED - The metrics for the specified window.

Metrics -> (structure)

Provides metrics used to evaluate the performance of a predictor.

RMSE -> (double)

The root-mean-square error (RMSE).

WeightedQuantileLosses -> (list)

An array of weighted quantile losses. Quantiles divide a probability distribution into regions of equal probability. The distribution in this case is the loss function.

(structure)

The weighted loss value for a quantile. This object is part of the Metrics object.

Quantile -> (double)

The quantile. Quantiles divide a probability distribution into regions of equal probability. For example, if the distribution was divided into 5 regions of equal probability, the quantiles would be 0.2, 0.4, 0.6, and 0.8.

LossValue -> (double)

The difference between the predicted value and the actual value over the quantile, weighted (normalized) by dividing by the sum over all quantiles.

ErrorMetrics -> (list)

Provides detailed error metrics for each forecast type. Metrics include root-mean square-error (RMSE), mean absolute percentage error (MAPE), mean absolute scaled error (MASE), and weighted average percentage error (WAPE).

(structure)

Provides detailed error metrics to evaluate the performance of a predictor. This object is part of the Metrics object.

ForecastType -> (string)

The Forecast type used to compute WAPE, MAPE, MASE, and RMSE.

WAPE -> (double)

The weighted absolute percentage error (WAPE).

RMSE -> (double)

The root-mean-square error (RMSE).

MASE -> (double)

The Mean Absolute Scaled Error (MASE)

MAPE -> (double)

The Mean Absolute Percentage Error (MAPE)

AverageWeightedQuantileLoss -> (double)

The average value of all weighted quantile losses.

IsAutoPredictor -> (boolean)

Whether the predictor was created with CreateAutoPredictor .

AutoMLOverrideStrategy -> (string)

Note

The LatencyOptimized AutoML override strategy is only available in private beta. Contact AWS Support or your account manager to learn more about access privileges.

The AutoML strategy used to train the predictor. Unless LatencyOptimized is specified, the AutoML strategy optimizes predictor accuracy.

This parameter is only valid for predictors trained using AutoML.

OptimizationMetric -> (string)

The accuracy metric used to optimize the predictor.