Struct AutoMlJobConfig

#[non_exhaustive]
pub struct AutoMlJobConfig {
    pub completion_criteria: Option<AutoMlJobCompletionCriteria>,
    pub security_config: Option<AutoMlSecurityConfig>,
    pub candidate_generation_config: Option<AutoMlCandidateGenerationConfig>,
    pub data_split_config: Option<AutoMlDataSplitConfig>,
    pub mode: Option<AutoMlMode>,
}

A collection of settings used for an AutoML job.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

completion_criteria: Option<AutoMlJobCompletionCriteria>

How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

security_config: Option<AutoMlSecurityConfig>

The security configuration for traffic encryption or Amazon VPC settings.

candidate_generation_config: Option<AutoMlCandidateGenerationConfig>

The configuration for generating a candidate for an AutoML job (optional).

data_split_config: Option<AutoMlDataSplitConfig>

The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

mode: Option<AutoMlMode>

The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

Implementations

impl AutoMlJobConfig

pub fn completion_criteria(&self) -> Option<&AutoMlJobCompletionCriteria>

How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

pub fn security_config(&self) -> Option<&AutoMlSecurityConfig>

The security configuration for traffic encryption or Amazon VPC settings.

pub fn candidate_generation_config( &self, ) -> Option<&AutoMlCandidateGenerationConfig>

The configuration for generating a candidate for an AutoML job (optional).

pub fn data_split_config(&self) -> Option<&AutoMlDataSplitConfig>

The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

pub fn mode(&self) -> Option<&AutoMlMode>

The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

impl AutoMlJobConfig

pub fn builder() -> AutoMlJobConfigBuilder

Creates a new builder-style object to manufacture AutoMlJobConfig.

Trait Implementations

impl Clone for AutoMlJobConfig

fn clone(&self) -> AutoMlJobConfig

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for AutoMlJobConfig

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

Formats the value using the given formatter.

impl PartialEq for AutoMlJobConfig

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl StructuralPartialEq for AutoMlJobConfig