Data Skeptic

AdaBoost is a canonical example of the class of AnyBoost algorithms that create ensembles of weak learners. We discuss how a complex problem like predicting restaurant failure (which is surely caused by different problems in different situations) might benefit from this technique.

Platform as a service is a growing trend in data science where services like fraud analysis and face detection can be provided via APIs. Such services turn the actual model into a black box to the consumer. But can the model be reverse engineered?

Florian Tramèr shares his work in this episode showing that it can. The paper Stealing Machine Learning Models via Prediction APIs is definitely worth your time to read if you enjoy this episode. Related source code can be found in https://github.com/ftramer/Steal-ML.

For machine learning models created with the random forest algorithm, there is no obvious diagnostic to inform you which features are more important in the output of the model. Some straightforward but useful techniques exist revolving around removing a feature and measuring the decrease in accuracy or Gini values in the leaves. We broadly discuss these techniques in this episode.

As cities provide bike sharing services, they must also plan for how to redistribute bicycles as they inevitably build up at more popular destination stations. In this episode, Hui Xiong talks about the solution he and his colleagues developed to rebalance bike sharing systems.

Random forest is a popular ensemble learning algorithm which leverages bagging both for sampling and feature selection. In this episode we make an analogy to the process of running a bookstore.

Jo Hardin joins us this week to discuss the ASA's Election Prediction Contest. This is a competition aimed at forecasting the results of the upcoming US presidential election competition. More details are available in Jo's blog post found here.

You can find some useful R code for getting started automatically gathering data from 538 via Jo's github and official contest details are available here. During the interview we also mention Daily Kos and 538.

The F1 score is a model diagnostic that combines precision and recall to provide a singular evaluation for model comparison. In this episode we discuss how it applies to selecting an interior designer.

Urban congestion effects every person living in a city of any reasonable size. Lewis Lehe joins us in this episode to share his work on downtown congestion pricing. We explore topics of how different pricing mechanisms effect congestion as well as how data visualization can inform choices.

You can find examples of Lewis's work at setosa.io. His paper which we discussed during the interview isDistance-dependent congestion pricing for downtown zones.

On this episode, we discuss State of California data which can be found at pems.dot.ca.gov.

Heteroskedasticity is a term used to describe a relationship between two variables which has unequal variance over the range. For example, the variance in the length of a cat's tail almost certainly changes (grows) with age. On the other hand, the average amount of chewing gum a person consume probably has a consistent variance over a wide range of human heights.

We also discuss some issues with the visualization shown in the tweet embedded below.

Our guest today is Michael Cuthbert, an associate professor of music at MIT and principal investigator of the Music21 project, which we focus our discussion on today.

Music21 is a python library making analysis of music accessible and fun. It supports integration with popular formats such as MIDI, MusicXML, Lilypond, and others. It's also well integrated with The Elvis Project, enabling users to import large volumes of music for easy analysis. Music21 is a great platform for musicologists and machine learning researchers alike to explore patterns and structure in music.