Struct aws_sdk_forecast::model::EvaluationParameters

#[non_exhaustive]
pub struct EvaluationParameters { /* private fields */ }

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.

Implementations

impl EvaluationParameters

pub fn number_of_backtest_windows(&self) -> Option<i32>

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

pub fn back_test_window_offset(&self) -> Option<i32>

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

impl EvaluationParameters

pub fn builder() -> Builder

Creates a new builder-style object to manufacture EvaluationParameters.

Trait Implementations

impl Clone for EvaluationParameters

fn clone(&self) -> EvaluationParameters

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for EvaluationParameters

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq<EvaluationParameters> for EvaluationParameters

fn eq(&self, other: &EvaluationParameters) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for EvaluationParameters