Model Hyper Parameters

Using this tab, you are able to optimize hyper parameters of algorithms used in the transformation chain.

Execute Tuning: When selected, enables model tuning and evaluation.

Validation Type: Tools used for tuning the model:

Cross validation: In cross-validation, you make a fixed number of folds (or partitions) of the data, run the analysis on each fold, and then average the overall error estimate.

Train Validation Split: Train Validation Split creates a single dataset pair.

When Train Validation Split is selected, specify value for Train Validation Ratio.

Number of Folds: Specifies the number of folds for cross validation. Must be greater than or equal to two. Default value is three.

Tuned Model Name: Name of the Model created after applying Hyper Parameter Training.

Description: Summary or short description of the model.

Tags: Tags to be associated with the model.

Version Comments: A note about the model version.

Metric for evaluation: Select the metric to be used for model evaluation.

Train Ratio: Ratio between train and validation data. Must be between zero and 1. Default is 0.75

Tuned Model Name: Model created after Hyper Parameter model training.

Connection Name: Connections are the service identifiers. A connection name can be selected from the list if you have created and saved connection details for Amazon S3 earlier. Or create one as explained in the topic - Amazon S3 Connection →

Bucket Name: S3 target bucket name is to be specified.

Path: File or directory path of the bucket should be specified where the model needs to be saved.

The path must end with * in case of directory.

Example: outdir/*

Proceed to next and enter the notes in the specified area.

Click on the SAVE button after entering all the information.