Raymond Fok and Daniel S. Weld in a recent Arxiv preprint:

We argue explanations are only useful to the extent that they allow a human decision maker to verify the correctness of an AI’s prediction, in contrast to other desiderata, e.g., interpretability or spelling out the AI’s reasoning process.

This does ring true to me: Put yourself into the position of an employee of Big Company Inc. whose task it is to allocate marketing budgets, to purchase product inventory, or to perform any other monetary decision as part of a business process. Her dashboard, powered by a data pipeline and machine learning model, suggests to increase TV ad spend in channel XYZ, or to order thousands of units of a seasonal product to cover the summer.

In her shoes, if you had to sign the check, what let’s you sleep at night: Knowing the model’s feature importances, or having verified the prediction’s correctness?

I’d prefer the latter, and the former only so much as it helps in the pursuit of verification. Feature importance alone, it is argued however, can’t determine correctness:

Here, we refer to verification of an answer as the process of determining its correctness. It follows that many AI explanations fundamentally cannot satisfy this desideratum […] While feature importance explanations may provide some indication of how much each feature influenced the AI’s decision, they typically do not allow a decision maker to verify the AI’s recommendation.

We want verifiability, but we cannot have it for most relevant supervised learning problems. The number of viewers of the TV ad are inherently unknown at prediction time, as is the demand for the seasonal product. These applications are in stark contrast to the maze example the authors provide, in which the explanation method draws the proposed path through the maze.

If verifiability is needed to complement human decision making, then this might be why one can get the impression of explanation washing of machine learning systems: While current explanation methods are the best we can do, they fall short of what is really needed to trust a system’s recommendation.

What can we do instead? We could start by showing the actual data alongside the recommendation. Making the data explorable. The observation in question can be put into the context of observations from the training data for which labels exist, essentially providing case-based explanations.

Ideally, any context provided to the model’s recommendation is not based on another model that adds another layer to be verified, but on hard actuals.

In the case of forecasting, simply visualizing the forecast alongside the historical observations can be extremely effective at establishing trust. When the time series is stable and shows clear patterns, a human actually can verify the the forecast’s correctness up to a point. And a human easily spots likely incorrect forecasts given historical data.

The need for verifiability makes me believe in building data products, not just a model.

Upol Ehsan ponders on Mastodon:

Explainable AI suffers from an epidemic. I call it Explainability Washing. Think of it as window dressing–techniques, tools, or processes created to provide the illusion of explainability but not delivering it.

Ah yes, slapping feature importance values onto a prediction and asking your users “Are you not entertained?”.

This thread pairs well with Rick Saporta’s presentation. Both urge you to focus solely on your user’s decision when deciding what to build.

You might have heard this one before: To build successful data products, focus on the decisions your customers make. But when was the last time you considered “how your work get[s] converted into action”?

At Data Council 2023, Rick Saporta lays out a framework of what data products to build and how to make them successful with customers. He goes beyond the platitudes, his advice sounds hard-earned.

Slides are good, talk is great.

False Positives and False Negatives are traditionally a topic in classification problems only. Which makes sense: There is no such thing as a binary target in forecasting, only a continuous range. There is no true and false, only a continuous scale of wrong. But there lives an MBA student in me who really likes 2-by-2 charts, so let’s come up with one for forecasting.

The {True,False}x{Positive,Negative} confusion matrix is the one opportunity for university professors to discuss the stakeholders of machine learning systems. The fact that a stakeholder might care more about reducing the number of False Positives and thus accepting a higher rate of False Negatives. Certain errors are more critical than others. That’s just as much the case in forecasting.

To construct the 2-by-2 of forecasting, the obvious place to start is the sense of “big errors are worse”. Let’s put that on the y-axis.

This gives us the False and “True” equivalents of forecasting. The “True” is in quotes because any deviation from the observed value is some error. But for the sake of the 2-by-2, let’s call small errors “True”.

Next, we need the Positive and Negative equivalents. When talking about stakeholder priorities, Positive and Negative differentiate the errors that are Critical from those that are Acceptable. Let’s put that on the x-axis.

While there might be other ways to define criticality¹, human perception of time series forecastability comes up as soon as users of your product inspect your forecasts. The human eye will detect apparent trends and seasonal patterns and project them into the future. If your model does not, and wrongly so, it raises confusion instead. Thus, forecasts of series predictable by humans will be critized, while forecasts of series with huge swings and more noise than signal are easily acceptable.

To utilize this notion, we require a model of human perception of time series forecastability. In a business context, where seasonality may be predominant, the seasonal naive method captures much of what is modelled by the human eye. It is also inherently conservative, as it does not overfit to recent fluctuations or potential trends. It assumes business will repeat as it did before.

Critical, then, are forecasts of series that the seasonal naive method, or any other appropriate benchmark, predicts with small error, while Acceptable are any forecasts of series that the seasonal naive method predicts poorly. This completes the x-axis.

With both axes defined, the quadrants of the 2-by-2 emerge. Small forecast model erros are naturally Acceptable True when the benchmark model fails, and large forecast model errors are Acceptable False when the benchmark model also fails. Cases of series that feel predictable and are predicted well are Critical True. Lastly, series that are predicted well by a benchmark but not by the forecast model are Critical False.

The Critical False group contains the series for which users expect a good forecast because they themselves can project the series into the future—but your model fails to deliver that forecast and does something weird instead. It’s the group of forecasts that look silly in your tool, the ones that cause you discomfort when users point them out.

Keep that group small.

Oldie but a goodie: A recording of Matthias Seeger’s presentation of “Bayesian Intermittent Demand Forecasting for Large Inventories” at NeurIPS 2016. The corresponding paper is a favorite of mine, but I only now stumbled over the presentation. It sparked an entire catalogue of work on time series forecasting by Amazon, and like few others called out the usefulness of sample paths.

What works at Google-scale is not the pattern most data scientists need to employ at their work. But the paper “On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models” is the kind of paper that we need more of: Thrilling reports of what works in practice.

Also, the authors do provide abstract lessons anyone can use, such as considering the constraints of your problem rather than using whatever is state-of-the-art:

A major design choice is how to represent an ad-query pair x. The semantic information in the language of the query and the ad headlines is the most critical component. Usage of attention layers on top of raw text tokens may generate the most useful language embeddings in current literature [64], but we find better accuracy and efficiency trade-offs by combining variations of fully-connected DNNs with simple feature generation such as bi-grams and n-grams on sub-word units. The short nature of user queries and ad headlines is a contributing factor. Data is highly sparse for these features, with typically only a tiny fraction of non-zero feature values per example.

From SAP’s Design Guidelines for Intelligent Systems:

High–stakes decisions are more common in a professional software environment than in everyday consumer apps, where the consequences of an action are usually easy to anticipate and revert. While the implications of recommending unsuitable educational content to an employee are likely to be minimal, recommendations around critical business decisions can potentially cause irreversible damage (for example, recommending an unreliable supplier or business partner, leading to the failure or premature termination of a project or contract). It’s therefore vital to enable users to take an informed decision.

While sometimes overlooked, this guide presents software in internal business processes as rich opportunity to augment human capabilities, deserving just as much love and attention as “everyday consumer apps”.

The chapters on intelligent systems are not too tuned to SAP systems, but they do have the specific context of business applications in mind which differentiates them from other (great!) guides on user interfaces for machine learning systems.

Based on that context, the guide dives deep on Ranking, Recommendations, and Matching, proving that it’s based on a much more hands-on view than any text discussing Supervised, Unsupervised, and Reinforcement Learning.

When aiming to build systems that “augment human capabilities”, the importance of “gain[ing] the user’s trust and foster[ing] successful adoption” can’t be overstated, making it worthwhile to deeply consider how we present the output of our systems.

Related: Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI by Alon Jacovi, Ana Marasović, Tim Miller, and Yoav Goldberg.

Russel Jacobs for Slate on the discontinued Dark Sky weather app, via Daring Fireball:

Indeed, Dark Sky’s big innovation wasn’t simply that its map was gorgeous and user-friendly: The radar map was the forecast. Instead of pulling information about air pressure and humidity and temperature and calculating all of the messy variables that contribute to the weather–a multi-hundred-billion-dollars-a-year international enterprise of satellites, weather stations, balloons, buoys, and an army of scientists working in tandem around the world (see Blum’s book)–Dark Sky simply monitored changes to the shape, size, speed, and direction of shapes on a radar map and fast-forwarded those images. “It wasn’t meteorology,” Blum said. “It was just graphics practice.”

Reminds me of DeepMind’s “Skilful precipitation nowcasting using deep generative models of radar”.

Huh, look at that, OpenAI’s ChatGPT portrays absolute confidence while giving plain wrong answers. But ChatGPT also does provide helpful responses a large number of times. So one kind of does want to use it. Sounds an awful lot like every other machine learning model deployed in 2022. But really, how do we turn fallible machine learning models into products to be used by humans? Not by injecting its answers straight into StackOverflow.

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The workshop will revolve around tools that automatically transform your data, in particular time series, into high-quality predictions based on AutoML and deep learning models. The event will be hosted by the team at AWS that develops AutoGluon, Syne Tune and GluonTS, and consist of a mix of tutorial-style presentation on the tools, discussion, and contributions from external partners on their applications.

Unique opportunity to hear from industry practitioners and GluonTS developers in person or by joining online.

Ryxcommar on Twitter:

I think one of the bigger mistakes people make when designing AI powered systems is seeing them as an AI first and foremost, and not as a system first and foremost.

Once you have your API contracts in place, the AI parts can be seen as function calls inside the system. Maybe your first version of these functions just return an unconditional expected value. But the system is the bulk of the work, the algorithm is a small piece.

To me, this is why regulation of AI (in contrast to regulation of software generally) can feel misguided: Any kind of function within a system has the potential to be problematic. It doesn’t have to use matrix multiplication for that to be the case.

More interestingly though, this is why it’s so effective to start with a simple model. It provides the function call around which you can build the system users care about.

Some free advice for data scientists– every time I have seen people treat their systems primarily as AI and not as systems, both the AI and the system suffered for it. Don’t make that mistake; design a system, not an “AI.”

The Berlin Bayesians meetup is happening again in-person. Juan Orduz is going to present Buy ‘Til You Die models implemented in PyMC:

In this talk, we introduce a certain type of customer lifetime models for the non-contractual setting commonly known as BTYD (Buy Till You Die) models. We focus on two sub-model components: the frequency BG/NBD model and the monetary gamma-gamma model. We begin by introducing the model assumptions and parameterizations. Then we walk through the maximum-likelihood parameter estimation and describe how the models are used in practice. Next, we describe some limitations and how the Bayesian framework can help us to overcome some of them, plus allowing more flexibility. Finally, we describe some ongoing efforts in the open source community to bring these new ideas and models to the public.

Buy ‘Til You Die models for the estimation of customer lifetime value were one of the first applications I worked on in my data science career, I’m glad to see they’re still around and kicking. Now implemented in the shiny new version of PyMC!

Edit: The event was rescheduled to September 27.

Some models are inherently interpretable because one can read their decision boundary right off them. In fact, you could call them interpreted as there is nothing left for you to interpret: The entire model is written out for you to read. For example, assume it’s July and we need to predict how many scoops of ice cream we’ll sell next month. The Seasonal Naive method tells us: AS 12 MONTHS AGO IN August, PREDICT sales = 3021.

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Yesterday morning, I retweeted this tweet by sklearn_inria that promotes a scikit-learn tutorial notebook on time-related feature engineering. It’s a neat notebook that shows off some fantastic ways of creating features to predict time series within a scikit-learn pipeline. There are, however, two things that irk me: All features of the dataset including the hourly weather are passed to the model. I don’t know the details of this dataset, but skimming what I believe to be its description on the OpenML repository, I suspect this might introduce data leakage as in reality we can’t know the exact hourly humidity and temperature days in advance.

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I don’t need to convince you that you should absolutely, to one hundred percent, quantify your forecast uncertainty—right? We agree about the advantages of using probabilistic measures to answer questions and to automate decision making—correct? Great. Then let’s dive a bit deeper. So you’re forecasting not just to fill some numbers in a spreadsheet, you are trying to solve a problem, possibly aiming to make optimal decisions in a process concerned with the future.

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Matt Henderson on Twitter (click through for the animation):

Be skeptical of the clusters shown in t-SNE plots! Here we run t-SNE on a 3d shape - it quickly invents some odd clusters and structures that aren’t really present in the original bunny.

What would happen if every machine learning method would come with a built-in visualization of the spurious results that it found?

Never mind the the answer to that question. I think that this dimensionality reduction of a 3D bunny into two dimensions isn’t even all that bad—the ears are still pretty cute. And it’s not like the original data had a lot more global and local structure once you consider that the bunny is not much more than noise in the shape of a rectangle with two ears that human eyes ascribe meaning to.

I’m the first to admit that t-SNE, UMAP, and all kinds of other methods will produce clusters from whatever data you provide. But so will k-means always return k clusters. One shouldn’t trust any model without some kind of evaluation of its results.

If you don’t take them at face value, UMAP and Co. can be powerful tools to explore data quickly and interactively. Look no further than the cool workflows Vincent Warmerdam is building for annotating text.

State space models are great, but they will fail in predictable ways. Well, claiming that they “fail” is a bit unfair. They actually behave exactly as they should given the input data. But if the input data fails to adhere to the Normal assumption or lacks stationarity, then this will affect the prediction derived from the state space models in perhaps unexpected yet deterministic ways. This article ensures that none of us is surprised by these “failure modes”.

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We present our solution for the M5 Forecasting - Uncertainty competition. Our solution ranked 6th out of 909 submissions across all hierarchical levels and ranked first for prediction at the finest level of granularity (product-store sales, i.e. SKUs). The model combines a multi-stage state-space model and Monte Carlo simulations to generate the forecasting scenarios (trajectories). Observed sales are modelled with negative binomial distributions to represent discrete over-dispersed sales. Seasonal factors are hand-crafted and modelled with linear coefficients that are calculated at the store-department level.

The approach chosen by this team of prior Lokad employees hits all the sweet spots. It’s simple, yet comes 6th in a Kaggle challenge, and produces multi-horizon sample paths.

Having the write-up of a well-performing result available in this detail is great—they share some nuggets:

Considering the small search space, this optimisation is done via grid search.

Easy to do for a two-parameter model and a neat trick to get computational issues under control. Generally neat to also enforce additional prior knowledge via arbitrary constraints on the search space.

According to the M5 survey by Makridakis et al. [3], our solution had the best result at the finest level of granularity (level 12 in the competition), commonly referred to as product-store level or SKU level (Stock Keeping Unit). For store replenishment and numerous other problems, the SKU level is the most relevant level.

Good on them to point this out. Congrats!

Google Maps and its Directions feature are the kind of data science product everyone wished they’d be building. It augments the user, enabling decision-making while driving. Directions exemplifies the difference between prediction and prescription. Google Maps doesn’t just expose data, and it doesn’t provide a raw analysis by-product like SHAP values. It processes historical and live data to predict the future and to optimize my route based on it, returning only the refined recommendations.

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Rob J. Hyndman gave a presentation titled “Uncertain futures: what can we forecast and when should we give up?” as part of the ACEMS public lecture series with recording available on Youtube.

He makes an often underappreciated point around minute 50 of the talk:

When the forecast uncertainty is too large to assist decision making? I don’t think that’s ever the case. Forecasting uncertainty being too large does assist decision making by telling the decision makers that the future is very uncertain and they should be planning for lots of different possible outcomes and not assuming just one outcome or another. And one of the problems we have in providing forecasts to decision makers is getting them to not focus in on the most likely outcome but to actually take into account the range of possibilities and to understand that futures are uncertain, that they need to plan for that uncertainty.

Data science projects are a tricky bunch. They entice you with challenging problems and promise a huge return if successful. In contrast to more traditional software engineering projects, however, data science projects entail more upfront uncertainty: You’ll not know until you tried whether the technology is good enough to solve the problem. Consequently, a data science endeavor fails more often, or doesn’t turn out to be the smash hit you and your stakeholders expected it to be.

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Along with their proposal for regulation of artificial intelligence, the EU published a definition of AI techniques. It includes everything, and that’s great!

From the proposal’s Annex I:

ARTIFICIAL INTELLIGENCE TECHNIQUES AND APPROACHES referred to in Article 3, point 1

• (a) Machine learning approaches, including supervised, unsupervised and reinforcement learning, using a wide variety of methods including deep learning;
• (b) Logic- and knowledge-based approaches, including knowledge representation, inductive (logic) programming, knowledge bases, inference and deductive engines, (symbolic) reasoning and expert systems;
• (c) Statistical approaches, Bayesian estimation, search and optimization methods.

Unsurprisingly, this definition and the rest of the proposal made the rounds: Bob Carpenter quipped about the fact that according to this definition, he has been doing AI for 30 years now (and that the EU feels the need to differentiate between statistics and Bayesian inference). In his newsletter, Thomas Vladeck takes the proposal apart to point out potential ramifications for applications. And Yoav Goldberg was tweeting about it ever since a draft of the document leaked.

From a data scientist’s point of view, this definition is fantastic: First, it highlights that AI is a marketing term used to sell whatever method does the job. Not including optimization as AI technique would have given everyone who called their optimizer “AI” a way to wiggle out of the regulation otherwise. This implicit acknowledgement is welcome.

Second, and more importantly, as practitioner it’s practical to have this “official” set of AI techniques in your backpocket for when someone asks what exactly AI is. The fact that one doesn’t have to use deep learning to wear the AI bumper sticker means that we can be comfortable in choosing the right tool for the job. At this point, AI refers less to a set of techniques or artificial intelligence, and more to a family of problems that are solved by one of the tools listed above.

In his interview with The Observer Effect, Tobi Lütke, CEO of Shopify, describes how Shopify benefits from resilient systems:

Most interesting things come from non-deterministic behaviors. People have a love for the predictable, but there is value in being able to build systems that can absorb whatever is being thrown at them and still have good outcomes.

So, I love Antifragile, and I make everyone read it. It finally put a name to an important concept that we practiced. Before this, I would just log in and shut down various servers to teach the team what’s now called chaos engineering.

But we’ve done this for a long, long time. We’ve designed Shopify very well because resilience and uptime are so important for building trust. These lessons were there in the building of our architecture. And then I had to take over as CEO.

It sticks out that Lütke uses “resilient” and “antifragile” interchangeably even though Taleb would point out that they are not the same thing: Whereas a resilient system doesn’t fail due to randomly turned off servers, an antifragile system benefits. (Are Shopify’s systems robust or have they become somehow better beyond robust due to their exposure to “chaos”?)

But this doesn’t diminish Lütke’s notion of resilience and uptime being “so important for building trust” (with users, presumably): Users’ trust in applications is fragile. Earning users’ trust in a tool that augments or automates decisions is difficult, and the trust is withdrawn quickly when the tool makes a stupid mistake. Making your tool robust against failure modes is how you make it trustworthy—and used.

Which makes it interesting to reason about what an equivalent to shutting off random servers is to machine learning applications (beyond shutting off the server running the model). Label noise? Shuffling features? Adding Covid-19-style disruptions to your time series? The latter might be more related to the idea of experimenting with a software system in production.

And—to return to the topic of discerning anti-fragile and robust—what would it mean for machine learning algorithms “to gain from disorder”? Dropout comes to mind. What about causal inference through natural experiments?

We demonstrate how recurrence plots can be used to embed a large set of time series via UMAP and HDBSCAN to quickly identify groups of series with unique characteristics such as seasonality or outliers. The approach supports exploratory analysis of time series via visualization that scales poorly when combined with large sets of related time series. We show how it works using a Walmart dataset of sales and a Citi Bike dataset of bike rides.

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This article derives the Local-Linear Trend specification of the Bayesian Structural Time Series model family from scratch, implements it in Stan and visualizes its components via tidybayes. To provide context, links to GAMs and the prophet package are highlighted. The code is available here. I tried to come up with a simple way to detect “outliers” in time series. Nothing special, no anomaly detection via variational auto-encoders, just finding values of low probability in a univariate time series.

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Suppose you are given a data set of five images to train on, and then have to classify new images with your trained model. Five training samples are in general not sufficient to train a state-of-the-art image classification model, thus this problem is hard and earned it’s own name: few-shot image classification. A lot has been written on few-shot image classification and complex approaches have been suggested.1 Tian et al.

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We treat the turn of the year as an intervention to infer the causal effect of New Year’s resolutions on McFit’s Google Trend index. By comparing the observed values from the treatment period against predicted values from a counterfactual model, we are able to derive the overall lift induced by the intervention. Throughout the year, people’s interest in a McFit gym membership appears quite stable.1 The following graph shows the Google Trend for the search term “McFit” in Germany for April 2017 to until the week of December 17, 2017.

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My satRday Berlin slides on “Modeling Short Time Series” are available here. This saturday, June 15, Berlin had its first satRday conference. I eagerly followed the hashtags of satRday Amsterdam last year and satRday Capetown the year before that on Twitter. Thanks to Noa Tamir, Jakob Graff, Steve Cunningham, and many others, we got a conference in Berlin as well. When I saw the call for papers, I jumped at the opportunity to present, trying what it feels like to be on the other side of the microphone; being in the hashtag instead of following it.

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I just published a longer case study, Modeling Short Time Series with Prior Knowledge: What ‘Including Prior Information’ really looks like. It is generally difficult to model time series when there is insuffient data to model a (suspected) long seasonality. We show how this difficulty can be overcome by learning a seasonality on a different, long related time series and transferring the posterior as a prior distribution to the model of the short time series.

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Last week, I gave a presentation about the concept of and intuition behind probabilistic programming and model-based machine learning in front of a general audience. You can read my extended notes here. Drawing on ideas from Winn and Bishop’s “Model-Based Machine Learning” and van de Meent et al.’s “An Introduction to Probabilistic Programming”, I try to show why the combination of a data-generating process with an abstracted inference is a powerful concept by walking through the example of a simple survival model.

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Back in 2003, Paul Graham, of Viaweb and Y Combinator fame, published an article entitled “Better Bayesian Filtering”. I was scrolling chronologically through his essays archive the other day when this article stuck out to me (well, the “Bayesian” keyword). After reading the first few paragraphs, I was a little disappointed to realize the topic was Naive Bayes rather than Bayesian methods. But it turned out to be a tale of implementing a machine learning solution for a real world application before anyone dared to mention AI in the same sentence.

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Videos of the talks given at the International Conference on Probabilistic Programming (PROBPROG 2018) back in October were published a few days ago and are now available on Youtube. I have not watched all presentations yet, but a lot of big names attended the conference so there should be something for everyone. In particular the talks by Brooks Paige (“Semi-Interpretable Probabilistic Models”) and Michael Tingley (“Probabilistic Programming at Facebook”) made me curious to explore their topics more.

One of the many fantastic workshops at ICML this year was the Exploration in Reinforcement Learning workshop. All talks were recorded and are now available on Youtube. Highlights include presentations by Ian Osband, Emma Brunskill, and Csaba Szepesvari, among others. You can find the workshop’s homepage here with more information and the accepted papers.

Building on the Instacart product recommendations based on Pointwise Mutual Information (PMI) in the previous article, we use Singular Value Decomposition to factorize the PMI matrix into a matrix of lower dimension (“embedding”). This allows us to identify groups of related products easily. We finished the previous article with a long table where every row measured how surprisingly often two products were bought together according to the Instacart Online Grocery Shopping dataset.

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Using pointwise mutual information, we create highly efficient “customers who bought this item also bought” style product recommendations for more than 8000 Instacart products. The method can be implemented in a few lines of SQL yet produces high quality product suggestions. Check them out in this Shiny app. Back in school, I was a big fan of the Detective Conan anime. For whatever reason, one of the episodes stuck with me.

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Using t-SNE, I wrote a Shiny app that recommends similar Pokémon. Try it out here. Needless to say, I was and still am a big fan of the Pokémon games. So I was very excited to see that a lot of the meta data used in Pokémon games is available on Github due to the Pokémon API project. Data on Pokémon’s names, types, moves, special abilities, strengths and weaknesses is all cleanly organized in a few dozen csv files.

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By now, some time has passed since NIPS 2016. Consequently, several recaps can be found on blogs. One of them is this one by Eric Jang. If you want to make your first steps in putting some of the theory presented at NIPS into practice, why not take a look at this slide deck about reinforcement learning in R? The RStudio Conference also took place, and apparently has been a blast.

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In a post on Tinder’s tech blog, Mike Hall presents a new application for multi-armed bandits. At Tinder, they started to use multi-armed bandits to optimize the photo of users that is shown first: While a user can have multiple photos in his profile, only one of them is shown first when another user swipes through the deck of user profiles. By employing an adapted epsilon-greedy algorithm, Tinder optimizes this photo for the “Swipe-Right-Rate”. Mike Hall about the project:

It seems to fit our problem perfectly. Let’s discover which profile photo results in the most right swipes, without wasting views on the low performing ones. …

We were off to a solid start with just a little tweaking and tuning. Now, we are able to leverage Tinder’s massive volume of swipes in order to get very good results in a relatively small amount of time, and we are convinced that Smart Photos will give our users a significant upswing in the number of right swipes they are receiving with more complex and fine-tuned algorithms as we move forward.

At Airbnb, the data science team has written their own R packages to scale with the company’s growth. The most basic achievement of the packages is the standardization of the work (ggplot and RMarkdown templates) and reduction of duplicate effort (importing data). New employees are introduced to the infrastructure with extensive workshops. This reminded me of a presentation by Hilary Parker in April at the New York R Conference on Scaling Analysis Responsibly.

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Christian Hennig provides a function called clusterboot() in his R package fpc which I mentioned before when talking about assessing the quality of a clustering. The function runs the same cluster algorithm on several bootstrapped samples of the data to make sure that clusters are reproduced in different samples; it validates the cluster stability. In a similar vein, the reproducibility of clusterings with subsequent use for marketing segmentation is discussed in this paper by Dolnicar and Leisch.

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During one of the talks at PyData Berlin, a presenter quickly mentioned a k-means clustering used to group similar clothing brands. She commented that it wasn’t perfect, but good enough and the result you would expect from a k-means clustering. There remains the question, however, how one can assess whether a clustering is “good enough”. In above case, the number of brands is rather small, and simply by looking at the groups one is able to assess whether the combination of Tommy Hilfiger and Marc O’Polo is sensible.

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I don’t know about you, but I think taxi data is fascinating. There is a lot you can do with the data sets as they usually contain observations on geolocation as well as time stamps besides other information, which makes them unique. Geolocation and timestamps alone, as well as the large number of observations in cities like New York enable you to create stunning visualizations that aren’t possible with any other set of data.

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Yet another day was spent working on the taxi data provided by the NYC Taxi and Limousine Commission (TLC). My goal in working with the data was to create a plot that maps the streets of New York using the geolocation data that is provided for the taxis’ pickup and dropoff locations as longitude and latitude values. So far, I had only used the dataset for January of 2015 to plot the locations; also, I hadn’t used the more than 12 million observations in January alone but a smaller sample (100000 to 500000 observations).

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