Documentation

A collection of settings used for an AutoML job.

See: newAutoMLJobConfig smart constructor.

Create a value of AutoMLJobConfig with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:candidateGenerationConfig:AutoMLJobConfig', autoMLJobConfig_candidateGenerationConfig - The configuration for generating a candidate for an AutoML job (optional).

$sel:completionCriteria:AutoMLJobConfig', autoMLJobConfig_completionCriteria - How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

$sel:dataSplitConfig:AutoMLJobConfig', autoMLJobConfig_dataSplitConfig - The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

$sel:mode:AutoMLJobConfig', autoMLJobConfig_mode - 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 will automatically select 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.

$sel:securityConfig:AutoMLJobConfig', autoMLJobConfig_securityConfig - The security configuration for traffic encryption or Amazon VPC settings.

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

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

The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

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 will automatically select 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.

The security configuration for traffic encryption or Amazon VPC settings.