review articles

doi:10.1145/2347736.2347755

Tapping into the “folk knowledge” needed to advance machine learning applications.

by Pedro domingos

a few useful

things to

Know about

machine

Learning

MACHINE LEARNING SYSTEMS automatically learn programs from data. This is often a very attractive alternative to manually constructing them, and in the last decade the use of machine learning has spread rapidly throughout computer science and beyond. Machine learning is used in Web search, spam filters, recommender systems, ad placement, credit scoring, fraud detection, stock trading, drug design, and many other applications. A recent report from the McKinsey Global Institute asserts that machine learning (a.k.a. data mining or predictive analytics) will be the driver of the next big wave of innovation.15 Several fine textbooks are available to interested practitioners and researchers (for example, Mitchell16 and Witten et al.24). However, much of the “folk knowledge” that

is needed to successfully develop machine learning applications is not readily available in them. As a result, many machine learning projects take much longer than necessary or wind up producing less-than-ideal results. Yet much of this folk knowledge is fairly easy to communicate. This is the purpose of this article.

key insights

machine learning algorithms can figure
out how to perform important tasks
by generalizing from examples. this is
often feasible and cost-effective where
manual programming is not. as more
data becomes available, more ambitious
problems can be tackled.

machine learning is widely used in computer science and other fields. however, developing successful machine learning applications requires a substantial amount of “black art” that is difficult to find in textbooks.

this article summarizes 12 key lessons that machine learning researchers and practitioners have learned. these include pitfalls to avoid, important issues to focus on, and answers to common questions.