Data Science Classroom: Naive Bayes and Logistic Regression

For our next Meetup, we're pleased to have Elena Zheleva presenting an introduction to two fundamental methods used in data analysis and prediction. And to celebrate the holiday season, and with thanks to our sponsors, we'll be serving egg nog along with tasty eats and even tastier data science!




Consider the problem of separating spam email from non-spam email, or predicting which... [read more]

For our next Meetup, we're pleased to have Elena Zheleva presenting an introduction to two fundamental methods used in data analysis and prediction. And to celebrate the holiday season, and with thanks to our sponsors, we'll be serving egg nog along with tasty eats and even tastier data science!




Consider the problem of separating spam email from non-spam email, or predicting which leads will become customers, or any other classification problem. Logistic Regression is a classical method from Statistics used to model the probability that data is in one of two categories. Naive Bayes is a very similar Machine Learning algorithm for categorization with independent predictors. Any Data Scientist should have a solid understanding of these two foundational methods, what they do, how they differ, and how to use them.
Elena Zheleva earned her PhD this year from the Computer Science Department at the University of Maryland, College Park. Her dissertation explored new methods for classification in social networks which consider both friendship links and social groups of users, as well as models for evolving social networks and groups, and the application of machine learning algorithms to privacy issues in social networks. She currently works in the Data Science group at LivingSocial in Washington, DC.

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