AutoML Wizards and Model Interpretation in H2O
- Predictive AI
- H2O Driverless AI
This lesson continues the Driverless AI walkthrough by comparing the standard predict flow with the Predict Wizard, a guided path built for domain experts who are newer to data science. It shows how a leaderboard run automatically launches multiple experiments with different accuracy, interpretability, and algorithm settings instead of requiring the user to choose one configuration. It then moves into interpreting a completed model, using SHAP values to find the most important feature driving predictions and partial dependence plots to see how changing that feature's value shifts the predicted outcome.
Key takeaways
- How the Predict Wizard adapts its questions based on a user's stated data science experience level
- What a leaderboard run does by automatically queuing multiple experiments with different accuracy and interpretability settings
- How SHAP values identify which feature contributes most to a model's predictions
- How partial dependence plots show the way a prediction changes as one feature's value increases or decreases
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