Parallelizing Linear Regression With MapReduce

Kevin Feasel

2018-06-25

R

Arthur Charpentier shows us the math behind using MapReduce to parallelize a linear regression:

Sometimes, with big data, matrices are too big to handle, and it is possible to use tricks to numerically still do the map. Map-Reduce is one of those. With several cores, it is possible to split the problem, to map on each machine, and then to aggregate it back at the end.

Arthur gives us an interesting example in R to boot.

Related Posts

Defining Tidy Data

John Mount shares thoughts about the concept of tidy data: A question is: is such a data set “tidy”? The paper itself claims the above definitions are “Codd’s 3rd normal form.” So, no the above table is not “tidy” under that paper’s definition. The the winner’s date of birth is a fact about the winner […]

Read More

Visualizing Earthquake Data

Giorgio Garziano continues a series on analyzing earthquake data: This is the third part of our post series about the exploratory analysis of a publicly available dataset reporting earthquakes and similar events within a specific 30 days time span. In this post, we are going to show static, interactive and animated earthquakes maps of different flavors by […]

Read More

Categories

June 2018
MTWTFSS
« May Jul »
 123
45678910
11121314151617
18192021222324
252627282930