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Back to the Future: Engineering Without Data (Models)

Software engineering has changed dramatically in the last few decades. The rise of AWS, high-level languages, powerful libraries, and frameworks increasingly allow engineers to focus on business logic. Today, software engineers spend much of their time writing code that reasons over data to show something or do something. But how

Noise: A Flaw in Book Writing

This is a review of Noise, A Flaw in Human Judgment by Kahneman, Sibony, and Sunstein. The phrase “noise in decision making” brings to mind “random” error. Scientists, however, shy away from random error. Science is mostly about systematic error, except, perhaps, quantum physics. So Kahneman et al. conceive of

Building Code: Making Government Code Publicly Available

Very little of the code that the government pays for is open-sourced. One of the reasons is that private companies would rather the code remain under wraps so that the errors never come to light, the price for producing software is never debated, and they get to continue to charge

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 (PowerPoint) presentations are unsuitable for serious problems. The essay is largely polemical, with Tufte freely mixing points about 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 decline in predictive accuracy