Ricky Ho has created a reference a 6-page PDF reference card on Big Data Machine Learning, with examples implemented in the R language. (A free registration to DZone Refcardz is required to download the PDF.) The examples cover: Predictive modeling overview (how to set up test and training sets in ... [Read more...]

I’ve been interviewed twice in the last year: For DecisionStats, 9 August 2012. For Data Mining Research, 21 October 2011. Republished in Amstat News, 1 December 2011. Some readers of this blog might find them interesting. I said a few things in t... [Read more...]

While most R packages on CRAN are designed to be used by an R user directly, a few packages are designed to be used by other package developers. (And some packages are so useful that they're regularly used by both camps.) When a package author publishes a package to CRAN, ... [Read more...]

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It has been two weeks since my last post on the 16th Euler problem. Now, since I just need a break after supper, I’m coming the 17th problem. If the numbers 1 to 5 are written out in words: one, … Continue reading → [Read more...]

Even though forums and question-and-answer services like StackOverflow are emerging as the place to find crowdsourced technical help when using software like R, the traditional r-help email list is still going strong. UCLA grad student and R user Richard Kwock presented a poster at last month's JSM conference with an ... [Read more...]

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I want to share a brilliant idea and a great example from the You’re Looking at the Wrong Number post at the GestaltU blog. Today, I will focus on the section of this post that outlines simple steps to improve a typical 60/40 stock/bond portfolio by using risk allocation ... [Read more...]

When running stats labs I like to allocate a slightly different subset of data to each student, which acts as an incentive for people to do their own work (rather than copying the same results from a fellow student). We … Continue reading → [Read more...]

Last week, Markus Gesmann used a log-linear model in R to predict the Olympic gold-medal winning 100m sprint time to be 9.68 seconds. The actual time was 9.63 seconds. Not bad! Meanwhile, the New York Times put Usuain Bolt's olympic record in context, comparing him in a virtual race with other gold ... [Read more...]

The 100m mean's sprint finals of the 2012 London Olympics are over and Usain Bolt won the gold medal again with a winning time of 9.63s. Time to compare the result with my forecast of 9.68s, posted on 22 July. My simple log-linear model predicted ... [Read more...]

Despite my preference for SAS over R, there are some add-ons to “basic” R that I’ve found that have made my learning process way easier.  While I’m still in my infancy in learning R, I feel like once I found … Continue reading →Getting Started Using R, Part 1: RStudio ... [Read more...]

KDnuggets recently posted its annual poll on data mining software, and the R language retains its #1 ranking as the most commonly-used software for data mining: R is now used by 52.5% of poll respondents, compared with 45% last year. Donnie Berkholz provides an analysis of the year-on-year trends for Redmonk. He provides ... [Read more...]

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Revolution Analytics is hosting several live and online courses over the next couple of months that will be of interest to R users looking to hone their skills: Visualization in R with ggplot2. Garrett Grolemund and Winston Chang instruct how to use the ggplot2 package to make, format, label and ... [Read more...]

Thank’s to Andrej who wrote this comment: “Is it possible to to color the resulting 12 clusters within your original image to get a feel for visual separation?” You can do so: But how to get values at a location? You will need these values to determine whether the defined ... [Read more...]

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The main question when using remote sensed raster data, as we do, is the question of NaN-treatment. Many R functions are able to use an option like rm.NaN=TRUE to treat these missing values. In our case the kmeans function in R is not capable to use such a ... [Read more...]

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Genetic algorithm is a search heuristic. GAs can generate a vast number of possible model solutions and use these to evolve towards an approximation of the best solution of the model. Hereby it mimics evolution in nature. GA generates a population, the individuals in this population (often called chromosomes) have  ... [Read more...]