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99/1 is the new 80/20

 ILLUSTRATION BY JACKIE FERRENTINO An obvious but often neglected fact is the overemphasized value of accuracy as a performance metric. In a two-class problem where 99% of the cases are of 0 (Not a spam email), achieving an accuracy of 99% is as easy as classifying all emails as safe. Sensitivity, specificity, and other metrics exist for a reason. The story of Waymo , Google's self-driving car, resembles the value of solving the remaining 1% of the problem where conventional machine learning gets stuck due to the limitations of training data. If 1% of the error turns into a make or break point, one needs to get creative. On a long tail that extends to infinity, walking faster or running does not probably help as much as a leap of imagination. I must note that it's not fair to expect an autonomous car to be "error-free" given we do not expect human drivers to perform error-free at the driver license exams and road tests. The two will just make different errors. #predic

When to normalize / apply weights

To me, this is interesting not because of the lack of transparency in methodology but the potential reason for the rankings to be wrong. I want to believe that this is a mistake not fraud, but really? Applying the weights before normalizing the scores? And the Bloomberg Businessweek spokesperson says "the magazine’s methodology was vetted by multiple data scientists." I have created a quick scenario as a reminder to my former (and current) students (posted in the comments as LinkedIn doesn't allow here). In the example, the scores are standardized across the five items (which are randomly generated and assigned weights). In the Businessweek rankings, standardization is supposed to be across institutions so that the weights proportionately affect each institution's score on the corresponding item. Nevertheless, the source of the error is the same. If the weights are applied before normalizing the data, the scores are adjusted by the weights disproportionately. Ranking

Algorithmic fashioning

PHOTOGRAPHER: JUSTIN CHIN/BLOOMBERG For years, Zara has been my go-to case to discuss data centricity in fashion retail. Zara is a staple example of how a focus on data and analytics combined with the right, complementary business processes can create wonders even in a market with high degrees of demand uncertainty due to the hedonic nature of consumption. Shein seems to be emerging as a contender, moving further into data-driven (not only data-informed) fast fashion. Its operation is also called real-time fashion rather than fast fashion. Shein doesn't own any physical stores (none at all) and ships all of its products directly from China. Bloomberg reports that "Shein has developed proprietary technology that harvests customers’ search data from the app and shares it with suppliers, to help guide decisions about design, capacity and production. It generates recommendations for raw materials and where to buy them, and gives suppliers access to a deep database of

“But it would be naïve to predict that unpredictable events won’t happen in the future.”

"Zillow Quits Home-Flipping Business, Cites Inability to Forecast Prices," WSJ reports.* I try to avoid passing along news stories but it's not everyday I receive a predictive analytics story as breaking news. I wonder whether the reason is really "an inability to forecast the prices" or "relying too much on an ability to forecast the prices" for a "$20 billion a year" venture as it was debuted. Zillow announced plans for this data-driven venture in 2018 by citing consumers who "expect magic to happen with a simple push of a button." In a statement yesterday, Zillow seems to have realized magic is not happening: “But it would be naïve to predict that unpredictable events won’t happen in the future.” Maybe it is never a good idea to develop a whole business model that grossly underestimates the changes in error (both reducible and irreducible ) due to potential bifurcations in market forces. * #nytimes coverage without a paywall #pre

If tech is everything, then it is nothing

ILLUSTRATION: GEORGE WYLESOL FOR BLOOMBERG BUSINESSWEEK What do #Facebook, #Tesla, #DoorDash, #Nvidia, and #GM* have in common? They are all "tech" companies. Alex Webb of Bloomberg offers a linguistic explanation for why technology ceased to be meaningful: "English lacked an equivalent to the French technique and German Technik. The English word “technique” hadn’t caught up with the innovations of the Industrial Revolution, and it still applied solely to the way in which an artist or artisan performed a skill." He contrasts technique as in "artistic technique" in English with technique as in "Lufthansa Technik" in German and argues that technology emerged in the early 20th century for the lack of a better alternative. Whether the reason is linguistic, sheer overhype, or semantic satiation, we may be better off dropping the "tech company" reference at this point unless it is elaborated further. For the companies that are more tech than

Data-driven paralysis

Data-driven decision making can lead to paralysis. Last week, the FDA and CDC committees couldn't make a decision about the booster shots because (complete) data was not available. Well, making decisions in the absence of complete data is a process of imagination and deep thinking, one that puts hypothesis development at the center and humans continue to prevail over machines in the process. To avoid such a paralysis, more focus can be put on developing and rethinking hypotheses and their likelihoods. In emergent problems, an in-depth discussion on hypotheses and likelihoods is probably more helpful than an obsession to access complete data. Otherwise, by defining complete data as a prerequisite, as it would be in data-driven decision making, we will continue to be paralyzed looking into the future. If we turn to data-informed decision making, however, hypotheses would take more control (not gut feeling but properly developed hypotheses*). We could then make decisions to be improve

To log or how to log

I avoid posting technical notes here. This is an exception because I have an agenda. Log transformation is widely used in modeling data for several reasons: Making data "behave," calculating elasticity etc. When an outcome variable naturally has zeros, however, log transformation is tricky. Many data modelers (including seasoned researchers) instinctively add a positive constant to each value in the outcome variable. One popular idea is to add 1 to the variable and transform raw zeros to log-transformed zeros. Another idea is to add a very small constant, especially when the scale of the outcome variable is small. Well, bad news is these are arbitrary choices and the resulting estimations may be biased. To me, if an analysis is correlational (as most are), a small bias may not be a big concern. If it is causal, and for example, an estimated elasticity will be used to take action (with an intention to change an outcome), that's trouble waiting to happen. This is a problem