How do companies extract meaning from the data they have?
In this chapter we hear from two people with very different approaches to that question—namely, William Cukierski from Kaggle and David Huffaker from Google.
Will went to Cornell for a BA in physics and to Rutgers to get his PhD in biomedical engineering. He focused on cancer research, studying pathology images. While working on writing his dissertation, he got more and more involved in Kaggle competitions (more about Kaggle in a bit), finishing very near the top in multiple competitions, and now works for Kaggle.
After giving us some background in data science competitions and crowdsourcing, Will will explain how his company works for the participants in the platform as well as for the larger community.
Will will then focus on feature extraction and feature selection. Quickly, feature extraction refers to taking the raw dump of data you have and curating it more carefully, to avoid the “garbage in, garbage out” scenario you get if you just feed raw data into an algorithm without enough forethought. Feature selection is the process of constructing a subset of the data or functions of the data to be the predictors or variables for your models and algorithms.
There is a history in the machine learning community of data science competitions—where individuals or teams compete over a period of several weeks or months to design a prediction ...