The workflow starts by loading the diabetes dataset and splitting it into training and testing sets using the Split node, with 80% of data used for training. The H2O Distributed Random Forest node trains the model, leveraging multiple decision trees to improve prediction accuracy and model robustness.

The trained model is evaluated on the test data using the H2O Score node, which produces validation metrics and diagnostic plots for performance assessment. The Print N Rows node displays a preview of predictions for easy verification.

This workflow delivers scalable and interpretable classification results, ideal for medical or categorical prediction tasks using distributed machine learning.