We here at Win-Vector LLC been working through an ad-hoc series about A/B testing combining elements of both operations research and statistical points of view. A dynamic programming solution to A/B test design Why does designing a simple A/B test seem so complicated? A clear picture of power and significance in A/B tests Bandit Formulations … Continue reading Sequential Analysis
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 … Continue reading Our Differential Privacy Mini-series
Authors: John Mount and Nina Zumel Nina and I were noodling with some variations of differentially private machine learning, and think we have found a variation of a standard practice that is actually fairly efficient in establishing differential privacy a privacy condition (but, as commenters pointed out- not differential privacy). Read on for the idea … Continue reading A simple differentially private-ish procedure
There remains a bit of a two-way snobbery that Frequentist statistics is what we teach (as so-called objective statistics remain the same no matter who works with them) and Bayesian statistics is what we do (as it tends to directly estimate posterior probabilities we are actually interested in). Nina Zumel hit the nail on the … Continue reading Baking priors
Win-Vector LLC‘s Nina Zumel wrote a great article explaining differential privacy and demonstrating how to use it to enhance forward step-wise logistic regression (essentially reusing test data). This allowed her to reproduce results similar to the recent Science paper “The reusable holdout: Preserving validity in adaptive data analysis”. The technique essentially protects and reuses test … Continue reading Using differential privacy to reuse training data
Differential privacy was originally developed to facilitate secure analysis over sensitive data, with mixed success. It’s back in the news again now, with exciting results from Cynthia Dwork, et. al. (see references at the end of the article) that apply results from differential privacy to machine learning. In this article we’ll work through the definition … Continue reading A Simpler Explanation of Differential Privacy
In some of my recent public talks (for example: here and here) I have mentioned a desire for “a deeper theory of fitting and testing.” I thought I would expand on what I meant by this. In this note I am going to cover a lot of different topics to try and suggest some perspective. … Continue reading A deeper theory of testing
Authors: John Mount (more articles) and Nina Zumel (more articles). Our four part article series collected into one piece. Part 1: The problem Part 2: In-training set measures Part 3: Out of sample procedures Part 4: Cross-validation techniques “Essentially, all models are wrong, but some are useful.” George Box Here’s a caricature of a data … Continue reading How do you know if your model is going to work?
Authors: John Mount (more articles) and Nina Zumel (more articles). In this article we conclude our four part series on basic model testing. When fitting and selecting models in a data science project, how do you know that your final model is good? And how sure are you that it’s better than the models that … Continue reading How do you know if your model is going to work? Part…
Authors: John Mount (more articles) and Nina Zumel (more articles). When fitting and selecting models in a data science project, how do you know that your final model is good? And how sure are you that it’s better than the models that you rejected? In this Part 3 of our four part mini-series “How do … Continue reading How do you know if your model is going to work? Part…
