Struct aws_sdk_sagemaker::types::AutoMlDataSplitConfig

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

This structure specifies how to split the data into train and validation datasets.

If you are using the V1 API (for example CreateAutoMLJob) or the V2 API for Natural Language Processing problems (for example CreateAutoMLJobV2 with a TextClassificationJobConfig problem type), the validation and training datasets must contain the same headers. Also, for V1 API jobs, the validation dataset must be less than 2 GB in size.

Implementations

impl AutoMlDataSplitConfig

pub fn validation_fraction(&self) -> Option<f32>

The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.

impl AutoMlDataSplitConfig

pub fn builder() -> AutoMlDataSplitConfigBuilder

Creates a new builder-style object to manufacture AutoMlDataSplitConfig.

Trait Implementations

impl Clone for AutoMlDataSplitConfig

fn clone(&self) -> AutoMlDataSplitConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for AutoMlDataSplitConfig

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

Formats the value using the given formatter.

impl PartialEq<AutoMlDataSplitConfig> for AutoMlDataSplitConfig

fn eq(&self, other: &AutoMlDataSplitConfig) -> 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 AutoMlDataSplitConfig