Conclusion
Summary
This Automatic Machine Learning Introduction with H2O Driverless AI tutorial taught us the basics of automated machine learning, the Driverless AI workflow, how to create and run an experiment using the DAI Wizard, visualize the experiment using different types of plots, feature engineering and optimization of the model, as well as to interpret the model and experiment results by generating an MLI report.
This tutorial is all you need to quickly and successfully get started with H2O Driverless AI and create your first model. In the following tutorials and courses, you will dig deeper into exploring more advanced functionality and usecases with H2O Driverless AI.
Next
It is recommended to proceed to the next tutorial in the learning path before exploring the final step of the Driverless AI workflow. The second tutorial in the learning path will provide a deeper understanding of Driverless AI's UI and its functionalities. The second tutorial is as follows:
Tutorial 1B: Machine Learning Experiment Scoring and Analysis - Financial Focus
This tutorial will be working with a subset of the Freddie Mac Single-Family Loan-Level Dataset to build a classification model. We will be exploring how to evaluate a DAI model through tools like ROC, Prec-Recall, Gain, Lift Charts, K-S Chart, and metrics such as AUC, F-Scores, GINI, MCC, and Log Loss.
If you want to test H2O Driverless AI without the constraints the Aquarium lab holds, such as the two-hour mark and no save work, you can request a 21-day trial license key for your own Driverless AI environment.
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