How do we tune hyperparameters in TIBCO Data Science Team Studio?

Operating System: Linux

Approach 1: Build a Workflow
      We use cross-validation sets and grid search to find the best parameter in some of the  AutoML operators. They are tuned in the modeling operators: AutoML logistic regression, AutoML Random Forest, and AutoML Gradient boosting trees. We can achieve the same with other regular operators by using the same ML operator multiple times in a workflow and passing a different range of values to get the best possible output.

Approach 2: Python code
     We can write a python code in Team Studio's python notebook that can find hyperparameters and call them in the workflow using a Python Execute operator.

 

We can tune hyperparameters in Team Studio either with a simple workflow or a python code.

We have attached a sample workflow and notebook for your reference.