Creating better models is a critical component to building a good data science product. It is relatively easy to build a first-cut machine-learning model, but what does it take to build a reasonably good or state-of-the-art model? Ensemble models—which help exploit the power of computing in searching the solution space.
Ensemble methods aren’t new. They form the basis for some extremely powerful machine learning algorithms like random forests and gradient boosting machines. The key point about ensemble is that consensus from diverse models are more reliable than a single source.
Bargava Subramanian discusses various strategies to build ensemble models, demonstrating how to combine model outputs from various base models (logistic regression, support vector machines, decision trees, neural networks, etc.) to create a stronger, better model output. Using an example package with bindings to both R and Python, Bargava covers bagging, boosting, stacking, and blending and draws on real-life examples from the enterprise world to explore where ensemble models can consistently produce better results when compared against the best-performing single models.
A preliminary version of the slides is available here.
Bargava Subramanian is a cofounder and deep learning engineer at Binaize in Bangalore, India. He has 15 years’ experience delivering business analytics and machine learning solutions to B2B companies. He mentors organizations in their data science journey. He holds a master’s degree from the University of Maryland, College Park. He’s an ardent NBA fan.
Amit Kapoor is a data storyteller at narrativeViz, where he uses storytelling and data visualization as tools for improving communication, persuasion, and leadership through workshops and trainings conducted for corporations, nonprofits, colleges, and individuals. Interested in learning and teaching the craft of telling visual stories with data, Amit also teaches storytelling with data for executive courses as a guest faculty member at IIM Bangalore and IIM Ahmedabad. Amit’s background is in strategy consulting, using data-driven stories to drive change across organizations and businesses. Previously, he gained more than 12 years of management consulting experience with A.T. Kearney in India, Booz & Company in Europe, and startups in Bangalore. Amit holds a BTech in mechanical engineering from IIT, Delhi, and a PGDM (MBA) from IIM, Ahmedabad.
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