Interface AutoMLAlgorithmConfig.Builder

All Superinterfaces:

Buildable, CopyableBuilder<AutoMLAlgorithmConfig.Builder,AutoMLAlgorithmConfig>, SdkBuilder<AutoMLAlgorithmConfig.Builder,AutoMLAlgorithmConfig>, SdkPojo

Enclosing class:

AutoMLAlgorithmConfig

public static interface AutoMLAlgorithmConfig.Builder
extends SdkPojo, CopyableBuilder<AutoMLAlgorithmConfig.Builder,AutoMLAlgorithmConfig>

Method Summary

Modifier and Type	Method	Description
AutoMLAlgorithmConfig.Builder	autoMLAlgorithms(Collection<AutoMLAlgorithm> autoMLAlgorithms)	The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
AutoMLAlgorithmConfig.Builder	autoMLAlgorithms(AutoMLAlgorithm... autoMLAlgorithms)	The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
AutoMLAlgorithmConfig.Builder	autoMLAlgorithmsWithStrings(String... autoMLAlgorithms)	The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
AutoMLAlgorithmConfig.Builder	autoMLAlgorithmsWithStrings(Collection<String> autoMLAlgorithms)	The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

Methods inherited from interface software.amazon.awssdk.utils.builder.CopyableBuilder

copy

Methods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilder

applyMutation, build

Methods inherited from interface software.amazon.awssdk.core.SdkPojo

equalsBySdkFields, sdkFields

Method Details

autoMLAlgorithmsWithStrings

AutoMLAlgorithmConfig.Builder autoMLAlgorithmsWithStrings(Collection<String> autoMLAlgorithms)

The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

• For the tabular problem type TabularJobConfig:

  Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

  • In ENSEMBLING mode:

    • "catboost"

    • "extra-trees"

    • "fastai"

    • "lightgbm"

    • "linear-learner"

    • "nn-torch"

    • "randomforest"

    • "xgboost"

  • In HYPERPARAMETER_TUNING mode:

    • "linear-learner"

    • "mlp"

    • "xgboost"

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

  • Choose your algorithms from this list.

    • "cnn-qr"

    • "deepar"

    • "prophet"

    • "arima"

    • "npts"

    • "ets"

Parameters:

autoMLAlgorithms - The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

• For the tabular problem type TabularJobConfig:

  Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

  • In ENSEMBLING mode:

    • "catboost"

    • "extra-trees"

    • "fastai"

    • "lightgbm"

    • "linear-learner"

    • "nn-torch"

    • "randomforest"

    • "xgboost"

  • In HYPERPARAMETER_TUNING mode:

    • "linear-learner"

    • "mlp"

    • "xgboost"

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

  • Choose your algorithms from this list.

    • "cnn-qr"

    • "deepar"

    • "prophet"

    • "arima"

    • "npts"

    • "ets"

Returns:

Returns a reference to this object so that method calls can be chained together.

autoMLAlgorithmsWithStrings

AutoMLAlgorithmConfig.Builder autoMLAlgorithmsWithStrings(String... autoMLAlgorithms)

The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

• For the tabular problem type TabularJobConfig:

  Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

  • In ENSEMBLING mode:

    • "catboost"

    • "extra-trees"

    • "fastai"

    • "lightgbm"

    • "linear-learner"

    • "nn-torch"

    • "randomforest"

    • "xgboost"

  • In HYPERPARAMETER_TUNING mode:

    • "linear-learner"

    • "mlp"

    • "xgboost"

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

  • Choose your algorithms from this list.

    • "cnn-qr"

    • "deepar"

    • "prophet"

    • "arima"

    • "npts"

    • "ets"

Parameters:

autoMLAlgorithms - The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

• For the tabular problem type TabularJobConfig:

  Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

  • In ENSEMBLING mode:

    • "catboost"

    • "extra-trees"

    • "fastai"

    • "lightgbm"

    • "linear-learner"

    • "nn-torch"

    • "randomforest"

    • "xgboost"

  • In HYPERPARAMETER_TUNING mode:

    • "linear-learner"

    • "mlp"

    • "xgboost"

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

  • Choose your algorithms from this list.

    • "cnn-qr"

    • "deepar"

    • "prophet"

    • "arima"

    • "npts"

    • "ets"

Returns:

Returns a reference to this object so that method calls can be chained together.

autoMLAlgorithms

AutoMLAlgorithmConfig.Builder autoMLAlgorithms(Collection<AutoMLAlgorithm> autoMLAlgorithms)

The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

• For the tabular problem type TabularJobConfig:

  Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

  • In ENSEMBLING mode:

    • "catboost"

    • "extra-trees"

    • "fastai"

    • "lightgbm"

    • "linear-learner"

    • "nn-torch"

    • "randomforest"

    • "xgboost"

  • In HYPERPARAMETER_TUNING mode:

    • "linear-learner"

    • "mlp"

    • "xgboost"

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

  • Choose your algorithms from this list.

    • "cnn-qr"

    • "deepar"

    • "prophet"

    • "arima"

    • "npts"

    • "ets"

Parameters:

autoMLAlgorithms - The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

• For the tabular problem type TabularJobConfig:

  Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

  • In ENSEMBLING mode:

    • "catboost"

    • "extra-trees"

    • "fastai"

    • "lightgbm"

    • "linear-learner"

    • "nn-torch"

    • "randomforest"

    • "xgboost"

  • In HYPERPARAMETER_TUNING mode:

    • "linear-learner"

    • "mlp"

    • "xgboost"

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

  • Choose your algorithms from this list.

    • "cnn-qr"

    • "deepar"

    • "prophet"

    • "arima"

    • "npts"

    • "ets"

Returns:

Returns a reference to this object so that method calls can be chained together.

autoMLAlgorithms

AutoMLAlgorithmConfig.Builder autoMLAlgorithms(AutoMLAlgorithm... autoMLAlgorithms)

The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

• For the tabular problem type TabularJobConfig:

  Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

  • In ENSEMBLING mode:

    • "catboost"

    • "extra-trees"

    • "fastai"

    • "lightgbm"

    • "linear-learner"

    • "nn-torch"

    • "randomforest"

    • "xgboost"

  • In HYPERPARAMETER_TUNING mode:

    • "linear-learner"

    • "mlp"

    • "xgboost"

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

  • Choose your algorithms from this list.

    • "cnn-qr"

    • "deepar"

    • "prophet"

    • "arima"

    • "npts"

    • "ets"

Parameters:

autoMLAlgorithms - The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

• For the tabular problem type TabularJobConfig:

  Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

  • In ENSEMBLING mode:

    • "catboost"

    • "extra-trees"

    • "fastai"

    • "lightgbm"

    • "linear-learner"

    • "nn-torch"

    • "randomforest"

    • "xgboost"

  • In HYPERPARAMETER_TUNING mode:

    • "linear-learner"

    • "mlp"

    • "xgboost"

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

  • Choose your algorithms from this list.

    • "cnn-qr"

    • "deepar"

    • "prophet"

    • "arima"

    • "npts"

    • "ets"

Returns:

Returns a reference to this object so that method calls can be chained together.