H2O created AI Tutorials out of inspiration for democratizing open source, distributed machine learning. Our tutorials are open to anyone in the community who would like to learn Distributed Machine Learning through step-by-step tutorials. Tutorials housed here are targeted at people of all skill levels.
If you run into problems that prevent you from completing a tutorial, head on over to H2O's Slack Channel and chat with Data Scientists from all over the world.
This tutorial is for Driverless AI; You'll explore how to: evaluate a DAI model through tools like, ROC, Prec-Recall, Gain and Lift Charts, K-S Chart as well as metrics such as AUC, F-Scores, GINI, MCC, and Log Loss.
This tutorial is for Driverless AI; You'll explore how to: launch an experiment, create ML Interpretability report, explore explainability concepts such as Global Shapley, partial dependence plot, decision tree surrogate, K-LIME, Local Shapley, LOCO and individual conditional expectation.