Copyright | (c) 2013-2023 Brendan Hay |
---|---|
License | Mozilla Public License, v. 2.0. |
Maintainer | Brendan Hay |
Stability | auto-generated |
Portability | non-portable (GHC extensions) |
Safe Haskell | Safe-Inferred |
Language | Haskell2010 |
Synopsis
- data AutoMLJobConfig = AutoMLJobConfig' {}
- newAutoMLJobConfig :: AutoMLJobConfig
- autoMLJobConfig_candidateGenerationConfig :: Lens' AutoMLJobConfig (Maybe AutoMLCandidateGenerationConfig)
- autoMLJobConfig_completionCriteria :: Lens' AutoMLJobConfig (Maybe AutoMLJobCompletionCriteria)
- autoMLJobConfig_dataSplitConfig :: Lens' AutoMLJobConfig (Maybe AutoMLDataSplitConfig)
- autoMLJobConfig_mode :: Lens' AutoMLJobConfig (Maybe AutoMLMode)
- autoMLJobConfig_securityConfig :: Lens' AutoMLJobConfig (Maybe AutoMLSecurityConfig)
Documentation
data AutoMLJobConfig Source #
A collection of settings used for an AutoML job.
See: newAutoMLJobConfig
smart constructor.
AutoMLJobConfig' | |
|
Instances
newAutoMLJobConfig :: AutoMLJobConfig Source #
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.
autoMLJobConfig_candidateGenerationConfig :: Lens' AutoMLJobConfig (Maybe AutoMLCandidateGenerationConfig) Source #
The configuration for generating a candidate for an AutoML job (optional).
autoMLJobConfig_completionCriteria :: Lens' AutoMLJobConfig (Maybe AutoMLJobCompletionCriteria) Source #
How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
autoMLJobConfig_dataSplitConfig :: Lens' AutoMLJobConfig (Maybe AutoMLDataSplitConfig) Source #
The configuration for splitting the input training dataset.
Type: AutoMLDataSplitConfig
autoMLJobConfig_mode :: Lens' AutoMLJobConfig (Maybe AutoMLMode) Source #
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.
autoMLJobConfig_securityConfig :: Lens' AutoMLJobConfig (Maybe AutoMLSecurityConfig) Source #
The security configuration for traffic encryption or Amazon VPC settings.