How Can We Trust Machine Learning? Exploration, Evaluation and Explanation for ML Models: Machine learning technologies are at the core of a new generation of intelligent applications that differentiate disruptive businesses from established players. Today, business tasks like product recommendation, image tagging, sentiment analysis, churn prediction, fraud detection and lead scoring can only be achieved using machine learning (ML). To build these applications at scale, companies are fast adopting tools such as Dato’s GraphLab Create and Predictive Services, enabling developers to accelerate the innovation cycle, and quickly take their ideas from inspiration to production.
Industry practitioners understand that in order to secure adoption of intelligent applications, they must build trust in their models and predictions – that is, gain confidence that their models are achieving their desired outcomes and a good understanding of how predictions are made.
In this talk, Carlos Guestrin, CEO of Dato, Inc. and Amazon Professor of Machine Learning at the University of Washington, describes:
- Recent research done at the University of Washington to provide a formal framework that explains why a machine learning model makes a particular prediction, and how even non-experts can use these explanations to improve the performance of a model.
- New tools introduced by Dato to help industry practitioners build trust and confidence in machine learning by making it easy to evaluate, explore, and explain models and predictions.
With these techniques, companies can start to have the means to gain trust and confidence in the models and predictions behind their core business applications.
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