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Class: Aws::ForecastService::Types::EvaluationParameters

Inherits:
Struct
  • Object
show all
Defined in:
gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb

Overview

Note:

When making an API call, you may pass EvaluationParameters data as a hash:

{
  number_of_backtest_windows: 1,
  back_test_window_offset: 1,
}

Parameters that define how to split a dataset into training data and testing data, and the number of iterations to perform. These parameters are specified in the predefined algorithms and can be overridden in the CreatePredictor request.

For example, suppose that you have a dataset with data collection frequency set to every day and you have 200 days worth of data (that is, 200 data points). Now suppose that you set the NumberOfBacktestWindows to 2 and the BackTestWindowOffset parameter to 20. The algorithm splits the data twice. The first time, the algorithm trains the model using the first 180 data points and uses the last 20 data points for evaluation. The second time, the algorithm trains the model using the first 160 data points and uses the last 40 data points for evaluation.

Instance Attribute Summary collapse

Instance Attribute Details

#back_test_window_offsetInteger

The point from the end of the dataset where you want to split the data for model training and evaluation. The value is specified as the number of data points.

Returns:

  • (Integer)


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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 1570

class EvaluationParameters < Struct.new(
  :number_of_backtest_windows,
  :back_test_window_offset)
  include Aws::Structure
end

#number_of_backtest_windowsInteger

The number of times to split the input data. The default is 1. The range is 1 through 5.

Returns:

  • (Integer)


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# File 'gems/aws-sdk-forecastservice/lib/aws-sdk-forecastservice/types.rb', line 1570

class EvaluationParameters < Struct.new(
  :number_of_backtest_windows,
  :back_test_window_offset)
  include Aws::Structure
end