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Make Government Code Publicly Available

Much of the software the public pays for never sees daylight. The reasons are rarely explained in procurement documents, but the opacity has costs. * Bugs linger longer. When the source is visible, independent auditors, academics, civic technologists, and rival vendors can reproduce builds, file issues, and submit fixes. Many eyes

Sampling Domain Knowledge

Say that we want to measure how often people go to risky websites. Let’s assume that the measurement of risk is expensive. We have data on how often people visit each domain on the web from a large sample. The number of unique domains in the data is large,

Pastations: Is it Time to Move Beyond Presentations?

In an influential essay, The Cognitive Style of PowerPoint, Tufte argues that presentations are unsuitable for serious problems. The essay is largely polemical, with Tufte freely mixing points about the affordances of the medium with criticisms of bad presentations and lazy broadsides. Hilarious stuff first: 1. “All 3 reports have

Homing in on the Home Advantage

A recent piece on ESPNCricinfo analyses the DRS data and argues that cricket should do away with neutral umpires. I reanalyzed the data. If a game is officiated by a home umpire, we expect the following: 1. Hosts will appeal less often as they are likely to be happier with

Nextdoor: Some Finer Points about KNN

The KNN classifier is one of the most intuitive ML algorithms. It predicts class by polling k nearest neighbors. Because it seems so simple, it is easy to miss a couple of the finer points: 1. Sample Splitting: Traditionally, when we split the sample, there is no peeking across samples.

Profit Maximizing Staffing

Say that there is a donation solicitation company. Say that there are 100M potential donors they can reach out to eachyear. Let’s also assume that the company gets paid on a contingency fee basis, getting a fixed percentage of all donations. The company currently follows the following process: it

Fooled by Randomness

Permutation-based methods for calculating variable importance and interpretation are increasingly common. Here are a few common places where they are used: Feature Importance (FI) The algorithm for calculating permutation-based FI is as follows: 1. Estimate a model 2. Permute a feature 3. Predict again 4. Estimate the decline in predictive

Monetizing Bad Models: Pay Per Correct Prediction

In many ML applications, especially ones where you need to train a model on customer data to get high levels of accuracy, the only models that ML SaaS companies can offer to a client out-of-the-box are bad. But many ML SaaS businesses hesitate to go to a client with a