Data Skeptic

Data Skeptic

Australia

Data Skeptic alternates between short mini episodes with the host explaining concepts from data science to his non-data scientist wife, and longer interviews featuring practitioners and experts on interesting topics related to data, all through the eye of scientific skepticism.

Episodes

MS Connect Conference  

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.

Causal Impact  

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/.

[MINI] The Bootstrap  

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.

[MINI] Gini Coefficients  

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.

Unstructured Data for Finance  

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.

[MINI] AdaBoost  

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.

Open Data Science Conference Santa Clara Meetup Announcement  

Kyle will be attending the Open Data Science Conference West in Santa Clara, November 4-6, 2016.

We're going to have an informal meetup on Saturday at 7pm at The Bourbon Pub (4900 Marie P DeBartolo Way).

There's no formal presentation, this is just a casual meetup. We'll congregate around the bar area. If you're attending the conference or simply in the area, drop by and say hello!

Stealing Models from the Cloud  

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.

[MINI] Calculating Feature Importance  

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.

NYC Bike Share Rebalancing  

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.

[MINI] Random Forest  

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.

Election Predictions  

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.

[MINI] F1 Score  

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  

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.

[MINI] Heteroskedasticity  

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.

Music21  

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.

[MINI] Paxos  

Paxos is a protocol for arriving a consensus in a distributed computing system which accounts for unreliability of the nodes.  We discuss how this might be used in the real world in the event of a massive disaster.

Trusting Machine Learning Models with LIME  

Machine learning models are often criticized for being black boxes. If a human cannot determine why the model arrives at the decision it made, there's good cause for skepticism. Classic inspection approaches to model interpretability are only useful for simple models, which are likely to only cover simple problems.

The LIME project seeks to help us trust machine learning models. At a high level, it takes advantage of local fidelity. For a given example, a separate model trained on neighbors of the example are likely to reveal the relevant features in the local input space to reveal details about why the model arrives at it's conclusion.

In this episode, Marco Tulio Ribeiro joins us to discuss how LIME (Locally Interpretable Model-Agnostic Explanations) can help users trust machine learning models. The accompanying paper is titled "Why Should I Trust You?": Explaining the Predictions of Any Classifier.

[MINI] ANOVA  

Analysis of variance is a method used to evaluate differences between the two or more groups.  It works by breaking down the total variance of the system into the between group variance and within group variance.  We discuss this method in the context of wait times getting coffee at Starbucks.

Machine Learning on Images with Noisy Human-centric Labels  

When humans describe images, they have a reporting bias, in that the report only what they consider important. Thus, in addition to considering whether something is present in an image, one should consider whether it is also relevant to the image before labeling it.

Ishan Misra joins us this week to discuss his recent paper Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels which explores a novel architecture for learning to distinguish presence and relevance. This work enables web-scale datasets to be useful for training, not just well groomed hand labeled corpora.

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