Foula Vagena – Ensemble Learning: Theory and Techniques

2021, diiP, Seminars + Hands-On Workshops

Foula Vagena
December 15, 4pm
online (zoom)

 

Abstract

Ensemble learning is the process by which multiple models, such as classifiers or experts, are combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the (classification, prediction, function approximation, etc.) performance of a model, or reduce the likelihood of an unfortunate selection of a poor one. By strategically combining multiple models one can produce a new predictive model with reduced variance, bias and improved predictions. In this tutorial we will explain the bias-variance tradeoff and describe how popular ensemble techniques (such as bagging, boosting, stacking etc) handle it. We will conclude the tutorial with an illustrative prediction task using various ensemble models.

The Hands-On Workshop will focus on examples of ensemble models.

References on the Booster subject as recommended during the seminar:

  • Boosting book: https://mitpress.mit.edu/books/boosting
  • XGBoost paper: https://arxiv.org/abs/1603.02754

 

Dr Foula Vagena
(Université Paris Cité, diiP)
Zografoula Vagena is a research associate at the Data Intelligence Institute of Paris (diiP) and affiliated with the Université Paris Cité. She has been a data science researcher and practitioner for over ten years. She has worked on different analytics problems including forecasting, image processing, graph analytics, multidimensional data analysis, text processing, recommendation systems, sequential data analysis and optimization within various fields such as transportation, healthcare, retail, finance/insurance and accounting. She has also performed research in the intersection of data management and analytics, and was a primary contributor of the MCDB/SimSQL systems that blended data management with Bayesian statistics. She holds a PhD in data management from the University of California, Riverside.

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