Publications by Joseph Rickert
Generating and Visualizing Multivariate Data with R
By Joseph Rickert The ability to generate synthetic data with a specified correlation structure is essential to modeling work. As you might expect, R’s toolbox of packages and functions for generating and visualizing data from multivariate distributions is impressive. The basic function for generating multivariate normal data is mvrnorm() from ...
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Analysing the movements of a cat
by Verena Haunschmid Since I have a cat tracker, I wanted to do some analysis of the behavior of my cats. I have shown how to do some of these things here. Data Collection The data was collected using the Tractive GPS Pet Tracker over a period of about one year from January 2014 to November 2014 (with breaks). From March to November I addition...
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Confidence Intervals for Random Forests
by Joseph Rickert Random Forests, the “go to” classifier for many data scientists, is a fairly complex algorithm with many moving parts that introduces randomness at different levels. Understanding exactly how the algorithm operates requires some work, and assessing how good a Random Forests model fits the data is a serious challenge. In the...
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Bay Area R User Group at Strata and PAW
by Joseph Rickert I always think of Strata Hadoop World and Predictive Analytics World as initiating the Spring conference season here in the San Francisco Bay Area. The rainy season is usually over by the end of March and it is a perfect time to visit. If you are traveling to either of these conferences from out of town and you are an R aficiona...
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Computing Classification Evaluation Metrics in R
by Said Bleik, Shaheen Gauher, Data Scientists at Microsoft Evaluation metrics are the key to understanding how your classification model performs when applied to a test dataset. In what follows, we present a tutorial on how to compute common metrics that are often used in evaluation, in addition to metrics generated from random classifiers, whi...
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Scoring R Models with Excel
by Joseph Rickert In a post late last year, my colleague and fellow blogger, Andrie de Vries described enhancements to the AzureML R package that makes it easy to publish R functions that consume data frames as Azure Web Services. A very nice consequence is that it is now feasible to develop predictive models in R and enable the Excel powered bus...
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Get ready for the New York R Conference
by Joseph Rickert Last year, I wrote the New York R Conference “set the bar pretty darn high for a first time conference”. Not only was there an outstanding lineup of speakers, but the energy and enthusiasm that conference attendees brought with them, or maybe just generated on the spot, was remarkable. This year I am definitely looking forw...
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Learning from Learning Curves
by Bob Horton, Senior Data Scientist, Microsoft This is a follow-up to my earlier post on learning curves. A learning curve is a plot of predictive error for training and validation sets over a range of training set sizes. Here we’re using simulated data to explore some fundamental relationships between training set size, model complexity, and...
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What’s new on CRAN: March 2016
by Joseph Rickert Packages continue to flood into CRAN at a rate the challenges the sanity of anyone trying to keep up with what's new. So far this month, more than 190 packages have been added. Here is a my view of what's interesting in this March madness. The launch_tutorial() function from the RtutoR package by Anup Nair launches a Shiny-ba...
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An Analysis of Traffic Violation Data with SQL Server and R
By Srini Kumar, Director of Data Science at Microsoft Who does not hate being stopped and given a traffic ticket? Invariably, we think that something is not fair that we got it and everyone else did not. I am no different, and living in the SF Bay Area, I have often wondered if I could get the data about traffic tickets, particularly since there ...
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