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Tutorial 1C: Building and interpreting a machine learning model with Python for credit default prediction

Introduction​

Overview​

In this tutorial, we will explore how to automate the process of building, analyzing, and interpreting machine learning models using H2O Driverless AI's Python API. This hands-on guide focuses on predicting credit default risk using the UCI Credit Card dataset, a popular dataset in financial modeling.

By following this tutorial, you will learn how to programmatically interact with H2O Driverless AI, from loading the dataset and preprocessing to configuring experiments, evaluating model diagnostics, and generating interpretability insights. Unlike GUI-driven workflows, this tutorial demonstrates a Python-centric approach, ideal for users who want to integrate Driverless AI into automated machine learning pipelines.

Objectives​

  • Authenticate and connect to a Driverless AI engine using Python.
  • Load the UCI Credit Card dataset.
  • Split the dataset into training and testing datasets using stratified sampling.
  • Configure and execute an experiment for credit risk modeling.
  • Evaluate model performance using diagnostic tools.
  • Gain insights into model behavior through interpretability tools.

Prerequisites​

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