Methods and Metrics for Cold-Start Recommendations

linear digression

You might sometimes find that it's hard to get started doing something, but once you're going, it gets easier.  Turns out machine learning algorithms, and especially recommendation engines, feel the same way.  The more they "know" about a user, like what movies they watch and how they rate them, the better they do at suggesting new movies, which is great until you realize that you have to start somewhere.  The "cold start" problem will be our focus in this episode, both the heuristic solutions that help deal with it and a bit of realism about the importance of skepticism when someone claims a great solution to cold starts.

Relevant links:
http://repository.upenn.edu/cgi/viewcontent.cgi?article=1141&context=cis_papers

The Cold Start Problem

Competing in a machine learning competition on Kaggle is a kind of rite of passage for data scientists.  Losing unexpectedly at the very end of the contest is also something that a lot of us have experienced.  It's not just bad luck: a very specific combination of overfitting on popular competitions can take someone who is in the top few spots in the final days of a contest and bump them down hundreds of slots in the final tally.

How to Lose at Kaggle

This week: everybody's favorite WWII-era classifier metric!  But it's not just for winning wars, it's a fantastic go-to metric for all your classifier quality needs.

Rock the ROC Curve

This week's episode is just in time for JupyterCon in NYC, August 22-25...

Jupyter notebooks are probably familiar to a lot of data nerds out there as a great open-source tool for exploring data, doing quick visualizations, and packaging code snippets with explanations for sharing your work with others.  If you're not a data person, or you are but you haven't tried out Jupyter notebooks yet, here's your nudge to go give them a try.  In this episode we'll go back to the old days, before notebooks, and talk about all the ways that data scientists like to work that wasn't particularly well-suited to the command line + text editor setup, and talk about how notebooks have evolved over their lifetime to become even more powerful and well-suited to the data scientist's workflow.

Jupyter Notebooks

As data science matures as a field, it's becoming clearer what attributes a data science team needs to have to elevate their work to the next level.  Most of our episodes are about the cool work being done by other people, but this one summarizes some thinking Katie's been doing herself around how to guide data science teams toward more mature, effective practices.  We'll go through five key characteristics of great data science teams, which we collectively refer to as "disciplined data science," and why they matter.

Disciplined Data Science

The Fourier transform is one of the handiest tools in signal processing for dealing with periodic time series data. Using a Fourier transform, you can break apart a complex periodic function into a bunch of sine and cosine waves, and figure out what the amplitude, frequency and offset of those component waves are. It's a really handy way of re-expressing periodic data--you'll never look at a time series graph the same way again.

The Fourier Transform

This is a re-release of an episode that originally aired on April 1, 2018

If you've done image recognition or computer vision tasks with a neural network, you've probably used a convolutional neural net. This episode is all about the architecture and implementation details of convolutional networks, and the tricks that make them so good at image tasks.

Convolutional Neural Networks

A message from Ben around algorithmic bias, and how our models are sometimes reflections of ourselves.

An interstitial word from Ben

As protests sweep across the United States in the wake of the killing of George Floyd by a Minneapolis police officer, we take a moment to dig into one of the ways that data science perpetuates and amplifies racism in the American criminal justice system. COMPAS is an algorithm that claims to give a prediction about the likelihood of an offender to re-offend if released, based on the attributes of the individual, and guess what: it shows disparities in the predictions for black and white offenders that would nudge judges toward giving harsher sentences to black individuals. 

We dig into this algorithm a little more deeply, unpacking how different metrics give different pictures into the “fairness” of the predictions and what is causing its racially disparate output (to wit: race is explicitly not an input to the algorithm, and yet the algorithm gives outputs that correlate with race—what gives?) Unfortunately it’s not an open-and-shut case of a tuning parameter being off, or the wrong metric being used: instead the biases in the justice system itself are being captured in the algorithm outputs, in such a way that a self-fulfilling prophecy of harsher treatment for black defendants is all but guaranteed. Like many other things this week, this episode left us thinking about bigger, systemic issues, and why it’s proven so hard for years to fix what’s broken.

Racism, the criminal justice system, and data science

This episode features Zach Drake, a working data scientist and PhD candidate in the Criminology, Law and Society program at George Mason University. Zach specializes in bringing data science methods to studies of criminal behavior, and got in touch after our last episode (about racially complicated recidivism algorithms). Our conversation covers a wide range of topics—common misconceptions around race and crime statistics, how methodologically-driven criminology scholars think about building crime prediction models, and how to think about policy changes when we don’t have a complete understanding of cause and effect in criminology. For the many of us currently re-thinking race and criminal justice, but wanting to be data-driven about it, this conversation with Zach is a must-listen.

Criminology and Data Science

This is a re-release of an episode that first ran on January 29, 2017.

This  week: everybody's favorite WWII-era classifier metric!  But it's not  just for winning wars, it's a fantastic go-to metric for all your  classifier quality needs.

Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.

https://hdsr.mitpress.mit.edu/pub/xsrt4zs2/release/2

The Data Science Open Source Ecosystem

This is a re-release of an episode that originally ran in October 2019.

If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each use case that are optimized for the needs and constraints of that use cases. You also wouldn’t be YouTube, which found themselves with this problem (gigantic data needs and several very different use cases of what they needed to do with that data) and went a different way: they built one analytics data system to serve them all. Procella, the system they built, is the topic of our episode today: by deconstructing the system, we dig into the four motivating uses of this system, the complexity they had to introduce to service all four uses simultaneously, and the impressive engineering that has to go into building something that “just works.”

Procella: YouTube's super-system for analytics data storage

A few weeks ago, we put out a call for data scientists interested in issues of race and racism, or people studying how those topics can be studied with data science methods, should get in touch to come talk to our audience about their work. This week we’re excited to bring on Todd Hendricks, Bay Area data scientist and a volunteer who reached out to tell us about his studies with the Stanford Open Policing dataset.

A Data Science Take on Open Policing Data

The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the healthcare process. This episode looks at two computer vision algorithms, one that diagnoses diabetic retinopathy and another that classifies liver cancer, and asks the question—are patients now getting better care, and achieving better outcomes, with these algorithms in the mix? The answer isn’t no, exactly, but it’s not a resounding yes, because these algorithms interact with a very complex system (the healthcare system) and other shortcomings of that system are proving hard to automate away. Getting a faster diagnosis from an image might not be an improvement if the image is now harder to capture (because of strict data quality requirements associated with the algorithm that wouldn’t stop a human doing the same job). Likewise, an algorithm getting a prediction mostly correct might not be an overall benefit if it introduces more dramatic failures when the prediction happens to be wrong. For every data scientist whose work is deployed into some kind of product, and is being used to solve real-world problems, these papers underscore how important and difficult it is to consider all the context around those problems.

A Reality Check on AI-Driven Medical Assistants

All good things must come to an end, including this podcast. This is the last episode we plan to release, and it doesn’t cover data science—it’s mostly reminiscing, thanking our wonderful audience (that’s you!), and marveling at how this thing that started out as a side project grew into a huge part of our lives for over 5 years.

It’s been a ride, and a real pleasure and privilege to talk to you each week. Thanks, best wishes, and good night!

—Katie and Ben

The Cold Start Problem

Linear Digressions

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