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Class: Aws::ForecastService::Types::EvaluationParameters
- Inherits:
-
Struct
- Object
- Struct
- Aws::ForecastService::Types::EvaluationParameters
- Defined in:
- (unknown)
Overview
When passing EvaluationParameters as input to an Aws::Client method, you can use a vanilla 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 but you can override them in the CreatePredictor request.
Returned by:
Instance Attribute Summary collapse
-
#back_test_window_offset ⇒ Integer
The point from the end of the dataset where you want to split the data for model training and testing (evaluation).
-
#number_of_backtest_windows ⇒ Integer
The number of times to split the input data.
Instance Attribute Details
#back_test_window_offset ⇒ Integer
The point from the end of the dataset where you want to split the data
for model training and testing (evaluation). Specify the value as the
number of data points. The default is the value of the forecast horizon.
BackTestWindowOffset
can be used to mimic a past virtual forecast
start date. This value must be greater than or equal to the forecast
horizon and less than half of the TARGET_TIME_SERIES dataset length.
ForecastHorizon
<= BackTestWindowOffset
< 1/2 *
TARGET_TIME_SERIES dataset length
#number_of_backtest_windows ⇒ Integer
The number of times to split the input data. The default is 1. Valid values are 1 through 5.