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Gaurav

https://gsood.com

Ruined by Google

Information on tap is a boon. But if it means that the only thing we will end up knowing—have in our heads—is where to go to find the information, it may also be a bane. Accessible stored cognitions are vital. They allow us to verify and contextualize new

The Benefit of Targeting

What is the benefit of targeting? Why (and when) do we need experiments to estimate the benefits of targeting? And what is the right baseline to compare against? I start with a business casual explanation, using examples to illustrate some of the issues at hand. Later in the note, I

AutoSum Plus

Nearly four years ago, I released autosum. Autosum exploits work by other scientists to harvest key points from (and key concerns with) a paper. The software grabs the sentence before or after the citation to build that knowledge. The output is pretty useful. See for yourself. But you could do

The Value of Predicting Bad Things

Foreknowledge of bad things is useful because it gives us an opportunity to a. prevent it, and b. plan for it. Let’s refine our intuitions with a couple of concrete examples. Many companies work super hard to predict customer ‘churn’—which customers are not going to use a product

Loss of Confidence

We all overestimate how much we know. If the aphorism, “the more you know, the more you know that you don’t know” is true, then how else could it be? But knowing more is not the only path to learning about our ignorance. Mistakes are another. When we make

Canonical Insights

If the canonical insight of computer science is automating repetition, the canonical insight of data science is optimization. It isn’t that computer scientists haven’t thought about optimization. They have. But computer scientists weren’t the first to think about automation, just like economists weren’t the first to

Computing Optimal Cut-Offs

Probabilities from classification models can have two problems: 1. Miscalibration: A p of .9 often doesn’t mean a 90% chance of 1 (assuming a dichotomous y). (You can calibrate it using isotonic regression.) 2. Optimal cut-offs: For multi-class classifiers, we do not know what probability value will maximize the