forecast/aws.sdk.kotlin.services.forecast.model/EvaluationParameters

EvaluationParameters

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.

Types

Builder

class Builder

Companion

object Companion

Properties

backTestWindowOffset

val backTestWindowOffset: Int?

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.

numberOfBacktestWindows

val numberOfBacktestWindows: Int?

The number of times to split the input data. The default is 1. Valid values are 1 through 5.

Functions

copy

inline fun copy(block: EvaluationParameters.Builder.() -> Unit = {}): EvaluationParameters

equals

open operator override fun equals(other: Any?): Boolean

hashCode

open override fun hashCode(): Int

toString

open override fun toString(): String