March 2016

Improve SVM Tuning through Parallelism

March 19, 2016 | statcompute

As pointed out in the chapter 10 of “The Elements of Statistical Learning”, ANN and SVM (support vector machines) share similar pros and cons, e.g. lack of interpretability and good predictive power. However, in contrast to ANN usually suffering from local minima solutions, SVM is always able to converge globally. ... [Read more...]

Using ProPublica “statefaces” in ggplot2

March 19, 2016 | hrbrmstr

I’m a huge fan of ProPublica. They have a super-savvy tech team, great reporters, awesome graphics folks and excel at data-driven journalism. Plus, they give away virtually everything, including data, text, graphics & tools. I was reading @USATODAY’s piece on lead levels in drinking water across America and saw ...
[Read more...]

Interactive Performance Evaluation of Binary Classifiers

March 19, 2016 | Anup Nair

Through this post I would like to describe a package that I recently developed and published on CRAN. The package titled IMP (Interactive Model Performance) enables interactive performance evaluation & comparison of (binary) classification models. There are a variety of different techniques available to assess model fit and to evaluate the ...
[Read more...]

Le Monde puzzle [#952]

March 18, 2016 | xi'an

A quite simple Le Monde mathematical puzzle again with Alice and Bob: In a multiple choice questionnaire with 50 questions, Alice gets a score s such that Bob can guess how many correct (+5 points), incorrect (-1 point) and missing (0 point) Alice got when adding that Alice could not have gotten s-2 ...
[Read more...]

Variography with gstat and ggplot2

March 18, 2016 | Bart Rogiers

Last year I wrote a short demo on variography with gstat and ggplot2 for a colleague who was planning to migrate to R. Just thought I’d share this here (with some additional stuff) as it might be useful for other people as well. First, make sure you have the ...
[Read more...]

Variography with gstat and ggplot2

March 18, 2016 | Bart Rogiers

Last year I wrote a short demo on variography with gstat and ggplot2 for a colleague who was planning to migrate to R. Just thought I’d share this here (with some additional stuff) as it might be useful for other people as well.First, make sure you have the ...
[Read more...]

More on preparing data

March 18, 2016 | John Mount

The Microsoft Data Science User Group just sponsored Nina Zumel‘s presentation “Preparing Data for Analysis Using R”. Microsoft saw Win-Vector LLC‘s ODSC West 2015 presentation “Prepping Data for Analysis using R” and generously offered to sponsor improving it and disseminating it to a wider audience. We feel Nina really ...
[Read more...]

Stacking the deck against treemaps

March 18, 2016 | hrbrmstr

So, I (unapologetically) did this to @Highcharts last week: @hrbrmstr Your loss of words inspired this post!! https://t.co/3KO0BP0k0u @hadleywickham @ma_salmon @tdmv @bearloga @rushworth_a @awhstin— Highcharts (@Highcharts) March 18, 2016 They did an awesome makeover (it’s interactive if you follow the link): And, I’...
[Read more...]

Creating a March Madness bracket with Machine Learning

March 18, 2016 | David Smith

March Madness is upon us here in the US. This annual college basketball competition pits 64 teams in a single-elimination tournament, and the team that goes undefeated for all 6 rounds will be named NCAA Champion. Predicting the winners of the competition, and in particular completing a "bracket" of the teams you ... [Read more...]

Hy-phen-ate All The Things! (in R)

March 18, 2016 | hrbrmstr

hyphenatr–what may be my smallest package ever–has just hit CRAN. It, well, hyphenates words using libhyphen (a.k.a. libhnj). There are no external dependencies (i.e. no brew install, apt get, et. al. required) and it compiles on everything CRAN supports including Windows. I started coding this ... [Read more...]

Classification on the German Credit Database

March 18, 2016 | arthur charpentier

In our data science course, this morning, we’ve use random forrest to improve prediction on the German Credit Dataset. The dataset is __ url="http://freakonometrics.free.fr/german_credit.csv" __ credit=read.csv(url, header = TRUE, sep = ",") Almost all variables are treated a numeric, but actually, most of them ...
[Read more...]
1 4 5 6 7 8 15

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)