The course emphasizes how to design A/B tests using prior “guestimates” of effect sizes (often you have these from prior campaigns, or somebody claims an effect size and it is merely your job to confirm it). It is fairly technical, and the emphasis is Bayesian- where we are trying to get an actual estimate of the distribution unknown true expected payoff rate of the various campaigns (the so-called posteriors). We show how to design and evaluate a sales campaigns for a product at two different price points.
Win-Vector LLC is starting a data science mailing list that we would like you to sign up for. It is going to be a (deliberately infrequent) set of updates including Win-Vector LLC notices, upcoming speaking events, and data science products.
We have two public appearances coming up in the next few weeks:
Workshop at ODSC, San Francisco – November 14
Both of us will be giving a two-hour workshop called Preparing Data for Analysis using R: Basic through Advanced Techniques. We will cover key issues in this important but often neglected aspect of data science, what can go wrong, and how to fix it. This is part of the Open Data Science Conference (ODSC) at the Marriot Waterfront in Burlingame, California, November 14-15. If you are attending this conference, we look forward to seeing you there!
You can find an abstract for the workshop, along with links to software and code you can download ahead of time, here.
An Introduction to Differential Privacy as Applied to Machine Learning: Women in ML/DS – December 2
I (Nina) will give a talk to the Bay Area Women in Machine Learning & Data Science Meetup group, on applying differential privacy for reusable hold-out sets in machine learning. The talk will also cover the use of differential privacy in effects coding (what we’ve been calling “impact coding”) to reduce the bias that can arise from the use of nested models. Information about the talk, and the meetup group, can be found here.
We’re looking forward to these upcoming appearances, and we hope you can make one or both of them.
We’ve just finished off a series of articles on some recent research results applying differential privacy to improve machine learning. Some of these results are pretty technical, so we thought it was worth working through concrete examples. And some of the original results are locked behind academic journal paywalls, so we’ve tried to touch on the highlights of the papers, and to play around with variations of our own.
A Simpler Explanation of Differential Privacy: Quick explanation of epsilon-differential privacy, and an introduction to an algorithm for safely reusing holdout data, recently published in Science (Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth, “The reusable holdout: Preserving validity in adaptive data analysis”, Science, vol 349, no. 6248, pp. 636-638, August 2015).
Note that Cynthia Dwork is one of the inventors of differential privacy, originally used in the analysis of sensitive information.
Using differential privacy to reuse training data: Specifically, how differential privacy helps you build efficient encodings of categorical variables with many levels from your training data without introducing undue bias into downstream modeling.
A bit of text we are proud to steal from our good friend Joseph Rickert:
Then, for some very readable background material on SVMs I recommend section 13.4 of Applied Predictive Modeling and sections 9.3 and 9.4 of Practical Data Science with R by Nina Zumel and John Mount. You will be hard pressed to find an introduction to kernel methods and SVMs that is as clear and useful as this last reference.
As readers have surely noticed the Win-Vector LLCblog isn’t a stream of short notes, but instead a collection of long technical articles. It is the only way we can properly treat topics of consequence.
What not everybody may have noticed is a number of these articles are serialized into series for deeper comprehension. The key series include:
Statistics to English translation.
This series tries to find vibrant applications and explanations of standard good statistical practices, to make them more approachable to the non statistician.
Statistics as it should be.
This series tries to cover cutting edge machine learning techniques, and then adapt and explain them in traditional statistical terms.
R as it is.
This series tries to teach the statistical programming language R “warts and all” so we can see it as the versatile and powerful data science tool that it is.