Check out these videos to learn about automatic feature engineering with driverless AI and machine learning interpretability with driverless AI.
1. Automatic Feature Engineering With Driverless AI:
Dmitry Larko, Kaggle Grandmaster and Senior Data Scientist at H2O.ai, showcases what he's doing with feature engineering, how he's doing it, and why it's important in the machine learning realm. He will delve into the workings of H2O.ai's new product, Driverless AI, whose automatic feature engineering increases the accuracy of models and frees up approximately 80% of the data practitioners' time — enabling them to draw actionable insights from the models built by Driverless AI. You will see:
- Overview of feature engineering.
- Real-time demonstration of feature engineering examples.
- Interpretation and reason codes of final models.
2. Machine Learning Interpretability With Driverless AI
In this video, Patrick showcases several approaches beyond the error measures and assessment plots typically used to interpret deep learning and machine learning models and results. Wherever possible, interpretability approaches are deconstructed into more basic components suitable for human storytelling: complexity, scope, understanding, and trust. You will see:
- Data visualization techniques for representing high-degree interactions and nuanced data structures.
- Contemporary linear model variants that incorporate machine learning and are appropriate for use in regulated industry.
- Cutting-edge approaches for explaining extremely complex deep learning and machine learning models.
That's it — enjoy!