Gojiberries (Page 18)

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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

Online Learning With Biased Sampling

Say that you train a model to predict who will click on an ad. Say that you deploy the model to only show ads to people who are likely to click on them. (For a discussion about the optimal strategy for whom to show ads to, see here.) And say

Some Facts About Indian Polling Stations

Of the 748,584 polling stations for which we have self-reported data on building conditions, nearly 24% report having Internet. A similar number report having “Landline Telephone/Fax Connection.” 97.7% report having toilets for men and women. 2.6% report being in a “dilapidated or dangerous” building. 93.2%

Growth Funnels

You spend a ton of time building a product to solve a particular problem. You launch. Then, either the kinds of people whose problem you are solving never arrive or they arrive and then leave. You want to know why because if you know the reason, you can work to

Conscious Uncoupling: Separating Compliance from Treatment

Let’s say that we want to measure the effect of a phone call encouraging people to register to vote on voting. Let’s define compliance as a person taking the call (like they do in Gerber and Green, 2000, etc.). And let’s assume that the compliance rate is

Prediction Errors: Using ML For Measurement

Say you want to measure how often people visit pornographic domains over some period. To measure that, you build a model to predict whether or not a domain hosts pornography. And let’s assume that for the chosen classification threshold, the False Positive rate (FP) is 10\% and the False