Data Skeptic

In this episode, we talk about a high-level description of deep learning.  Kyle presents a simple game (pictured below), which is more of a puzzle really, to try and give  Linh Da the basic concept.     Thanks to our sponsor for this week, the Data Science Association. Please check out their upcoming Dallas conference at dallasdatascience.eventbrite.com

Versioning isn't just for source code. Being able to track changes to data is critical for answering questions about data provenance, quality, and reproducibility. Daniel Whitenack joins me this week to talk about these concepts and share his work on Pachyderm. Pachyderm is an open source containerized data lake. During the show, Daniel mentioned the Gopher Data Science github repo as a great resource for any data scientists interested in the Go language. Although we didn't mention it, Daniel also did an interesting analysis on the 2016 world chess championship that complements our recent episode on chess well. You can find that post here Supplemental music is Lee Rosevere's Let's Start at the Beginning.   Thanks to Periscope Data for sponsoring this episode. More about them at periscopedata.com/skeptics      

Logistic Regression is a popular classification algorithm. In this episode, we discuss how it can be used to determine if an audio clip represents one of two given speakers. It assumes an output variable (isLinhda) is a linear combination of available features, which are spectral bands in the discussion on this episode.   Keep an eye on the dataskeptic.com blog this week as we post more details about this project.   Thanks to our sponsor this week, the Data Science Association.  Please check out their upcoming conference in Dallas on Saturday, February 18th, 2017 via the link below.   dallasdatascience.eventbrite.com  

Prior work has shown that people's response to competition is in part predicted by their gender. Understanding why and when this occurs is important in areas such as labor market outcomes. A well structured study is challenging due to numerous confounding factors. Peter Backus and his colleagues have identified competitive chess as an ideal arena to study the topic. Find out why and what conclusions they reached. Our discussion centers around Gender, Competition and Performance: Evidence from Real Tournaments from Backus, Cubel, Guid, Sanchez-Pages, and Mañas. A summary of their paper can also be found here.  

Deep learning can be prone to overfit a given problem. This is especially frustrating given how much time and computational resources are often required to converge. One technique for fighting overfitting is to use dropout. Dropout is the method of randomly selecting some neurons in one's network to set to zero during iterations of learning. The core idea is that each particular input in a given layer is not always available and therefore not a signal that can be relied on too heavily.  

In this episode I speak with Clarence Wardell and Kelly Jin about their mutual service as part of the White House's Police Data Initiative and Data Driven Justice Initiative respectively. The Police Data Initiative was organized to use open data to increase transparency and community trust as well as to help police agencies use data for internal accountability. The PDI emerged from recommendations made by the Task Force on 21st Century Policing. The Data Driven Justice Initiative was organized to help city, county, and state governments use data-driven strategies to help low-level offenders with mental illness get directed to the right services rather than into the criminal justice system.

We close out 2016 with a discussion of a basic interview question which might get asked when applying for a data science job. Specifically, how a library might build a model to predict if a book will be returned late or not.  

Today's episode is a reading of Isaac Asimov's Franchise.  As mentioned on the show, this is just a work of fiction to be enjoyed and not in any way some obfuscated political statement.  Enjoy, and happy holidays!

Classically, entropy is a measure of disorder in a system. From a statistical perspective, it is more useful to say it's a measure of the unpredictability of the system. In this episode we discuss how information reduces the entropy in deciding whether or not Yoshi the parrot will like a new chew toy. A few other everyday examples help us examine why entropy is a nice metric for constructing a decision tree.

Cloud services are now ubiquitous in data science and more broadly in technology as well. This week, I speak to Mark Souza, Tobias Ternström, and Corey Sanders about various aspects of data at scale. We discuss the embedding of R into SQLServer, SQLServer on linux, open source, and a few other cloud topics.

Today's episode is all about Causal Impact, a technique for estimating the impact of a particular event on a time series. We talk to William Martin about his research into the impact releases have on app and we also chat with Karen Blakemore about a project she helped us build to explore the impact of a Saturday Night Live appearance on a musician's career. Martin's work culminated in a paper Causal Impact for App Store Analysis. A shorter summary version can be found here. His company helping app developers do this sort of analysis can be found at crestweb.cs.ucl.ac.uk/appredict/.

The Bootstrap is a method of resampling a dataset to possibly refine it's accuracy and produce useful metrics on the result. The bootstrap is a useful statistical technique and is leveraged in Bagging (bootstrap aggregation) algorithms such as Random Forest. We discuss this technique related to polling and surveys.

The Gini Coefficient (as it relates to decision trees) is one approach to determining the optimal decision to introduce which splits your dataset as part of a decision tree. To pick the right feature to split on, it considers the frequency of the values of that feature and how well the values correlate with specific outcomes that you are trying to predict.

Financial analysis techniques for studying numeric, well structured data are very mature. While using unstructured data in finance is not necessarily a new idea, the area is still very greenfield. On this episode,Delia Rusu shares her thoughts on the potential of unstructured data and discusses her work analyzing Wikipedia to help inform financial decisions. Delia's talk at PyData Berlin can be watched on Youtube (Estimating stock price correlations using Wikipedia). The slides can be found here and all related code is available on github.

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.