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Tutorial 4B: Building a classification model for credit card clients information with H2O Driverless AI

Introduction​

Overview​

In this tutorial, you'll learn how to build a classification model using H2O Driverless AI, focusing on predicting the likelihood of credit card clients defaulting on their next payment.

We'll leverage the widely-used Default of Credit Card Clients Dataset to demonstrate how to develop and interpret a classification model. Unlike prior tutorials that emphasized experimentation, this one will center on machine learning interpretability (MLI). You'll explore the MLI tools and techniques provided by H2O Driverless AI to extract meaningful insights and ensure transparency in your model’s predictions.

Landing page of an MLI report

This report is particularly valuable for enhancing machine learning interpretability, especially in regulated industries where transparency and explanation are crucial. With a model's MLI report, you can uncover valuable insights into its inner workings, such as feature importance, partial dependence plots, and other performance metrics. This process can empower you to understand better and trust the predictions made by the model while offering you deeper insights into your built model and empowering you to make informed decisions in machine learning applications.

Objectives​

  • Build a classification model using H2O Driverless AI to predict credit card clients' probability of default.
  • MLI report: Learn how to generate and explore an MLI report for an H2O Driverless AI model.
  • Decision tree surrogate model: Learn how to interpret a decision tree surrogate model of an H2O Driverless AI model.

Prerequisites​

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