Gojiberries (Page 27)

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Stereotypical Understanding

The paucity of women in Computer Science, Math, and Engineering in the US is justly widely lamented. Sometimes, the imbalance is attributed to gender stereotypes. But only a small fraction of men study these fields. And in absolute terms, the proportion of women in these fields is not a great

Sample This: Sampling Randomly from the Streets

Say you want to learn about the average number of potholes per unit paved street in a city. To estimate that quantity, the following sampling plan can be employed: 1. Get all the streets in a city from Google Maps or OSM 2. Starting from one end of the street,

Clustering and then Classifying: Improving Prediction for Crudely-labeled and Mislabeled Data

Mislabeled and crudely labeled data are common problems in data science. Supervised prediction of such data is expectedly to yield poor results—coefficients are biased, and accuracy with regard to the true label is poor. One solution to the problem is to hand-code labels of an `adequate’ sample and infer

The Missing Plot

Datasets often contain missing values. And often enough—at least in social science data—values are missing systematically. So how do we visualize missing values? After all, they are missing. Some analysts simply list-wise delete points with missing values. Others impute, replacing missing values with mean or median. Yet others

About 85% Problematic: The Trouble With Human-In-The-Loop ML Systems

MIT researchers recently unveiled a system that combines machine learning with input from users to ‘predict 85% of the attacks.’ Each day, the system winnows down millions of rows to a few hundred atypical data points and passes these points on to ‘human experts’ who then label the few hundred