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.
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