From "Developing a Practical Forecasting Screener for Domestic Violence Incidents": "We had data from the screening instrument for 671 households. ... For the

From "Developing a Practical Forecasting Screener for Domestic Violence Incidents": "We had data from the screening instrument for 671 households. ... For the 516 households with complete data, there was at least one return call for 109. Thus, about 21% had a return call within three months after the screener information was collected. Although there is no guarantee that all of these calls were for domestic violence incidents, no doubt most were." Admittedly this is one paper, and not necessarily related to the system he created for parole prediction, but this would be a worryingly small dataset (and likely bad set of assumptions) for any data scientist, machine learning researcher, or machine learning practitioner. As Francois Chollet noted on Twitter, it's very reminiscent of the random forest approach to launching drone strikes[2] that had insanely small amounts of data: "The NSA evaluates the SKYNET program using a subset of 100,000 randomly selected people (identified by their MSIDN/MSI pairs of their mobile phones), and a a known group of seven terrorists. The NSA then trained the learning algorithm by feeding it six of the terrorists and tasking SKYNET to find the seventh." [1]: https://www.semanticscholar.org/paper/Developing-a-Practical-Forecasting-Screener-for-Berk-He/6999981067428dafadd10aa736e4b5c293f89823 https://www.semanticscholar.org/paper/Developing-a-Practical... [2]: http://arstechnica.co.uk/security/2016/02/the-nsas-skynet-program-may-be-killing-thousands-of-innocent-people/ http://arstechnica.co.uk/security/2016/02/the-nsas-skynet-pr...