Debugging machine learning models
Who is this presentation for?
- Data scientists
Prediction by machine learning models is fundamentally the execution of a computer program. In this case, the rules of the computer program are learned by the computer itself from training data instead of being programmed by a human. Like all good programs, machine learning models should be debugged to discover and remediate errors.
When the debugging process increases accuracy in holdout data, increases transparency into model mechanisms, decreases or identifies hackable attack surfaces, or decreases disparate impact, this debugging process also enhances trust and interpretability in model mechanisms and predictions. Navdeep Gill identifies several standard techniques in the context of model debugging—disparate impact, residual, and sensitivity analysis—and introduces novel applications such as global and local explanation of model residuals.
- A basic understanding of machine learning
What you'll learn
- Experience debugging machine learning models
Navdeep Gill is a senior data scientist and software engineer at H2O.ai, where he focuses mainly on machine learning interpretability and had focused on GPU-accelerated machine learning, automated machine learning, and the core H2O-3 platform. Previously, Navdeep focused on data science and software development at Cisco and was a researcher and analyst in several neuroscience labs at California State University, East Bay, University of California, San Francisco, and Smith Kettlewell Eye Research Institute. Navdeep earned an MS in computational statistics, a BS in statistics, and a BA in psychology (with a minor in mathematics) from California State University, East Bay.
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