interface SolutionConfigProperty
Language | Type name |
---|---|
![]() | Amazon.CDK.AWS.Personalize.CfnSolution.SolutionConfigProperty |
![]() | github.com/aws/aws-cdk-go/awscdk/v2/awspersonalize#CfnSolution_SolutionConfigProperty |
![]() | software.amazon.awscdk.services.personalize.CfnSolution.SolutionConfigProperty |
![]() | aws_cdk.aws_personalize.CfnSolution.SolutionConfigProperty |
![]() | aws-cdk-lib » aws_personalize » CfnSolution » SolutionConfigProperty |
Describes the configuration properties for the solution.
Example
// The code below shows an example of how to instantiate this type.
// The values are placeholders you should change.
import { aws_personalize as personalize } from 'aws-cdk-lib';
declare const autoMlConfig: any;
declare const hpoConfig: any;
const solutionConfigProperty: personalize.CfnSolution.SolutionConfigProperty = {
algorithmHyperParameters: {
algorithmHyperParametersKey: 'algorithmHyperParameters',
},
autoMlConfig: autoMlConfig,
eventValueThreshold: 'eventValueThreshold',
featureTransformationParameters: {
featureTransformationParametersKey: 'featureTransformationParameters',
},
hpoConfig: hpoConfig,
};
Properties
Name | Type | Description |
---|---|---|
algorithm | { [string]: string } | IResolvable | Lists the algorithm hyperparameters and their values. |
auto | any | The AutoMLConfig object containing a list of recipes to search when AutoML is performed. |
event | string | Only events with a value greater than or equal to this threshold are used for training a model. |
feature | { [string]: string } | IResolvable | Lists the feature transformation parameters. |
hpo | any | Describes the properties for hyperparameter optimization (HPO). |
algorithmHyperParameters?
Type:
{ [string]: string } |
IResolvable
(optional)
Lists the algorithm hyperparameters and their values.
autoMlConfig?
Type:
any
(optional)
The AutoMLConfig object containing a list of recipes to search when AutoML is performed.
eventValueThreshold?
Type:
string
(optional)
Only events with a value greater than or equal to this threshold are used for training a model.
featureTransformationParameters?
Type:
{ [string]: string } |
IResolvable
(optional)
Lists the feature transformation parameters.
hpoConfig?
Type:
any
(optional)
Describes the properties for hyperparameter optimization (HPO).