Publications by Joseph Rickert
How do you know if your model is going to work? Part 2: In-training set measures
by John Mount (more articles) and Nina Zumel (more articles) When fitting and selecting models in a data science project, how do you know that your final model is good? And how sure are you that it's better than the models that you rejected? In this Part 2 of our four part mini-series “How do you know if your model is going to work?” we devel...
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The New Microsoft Data Science User Group Program
by Joseph Rickert We are very pleased to announce that Microsoft will not only continue the Revolution Analytics’ tradition of supporting R user groups worldwide, but is expanding the scope of the user group program. The new 2016 Microsoft Data Science User Group Sponsorship Program is open to all user groups that are passionate about open-sour...
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Reading Financial Time Series Data with R
by Joseph Rickert In a recent post focused on plotting time series with the new dygraphs package, I did not show how easy it is to read financial data into R. However, in a thoughtful comment to the post, Achim Zeileis pointed out a number of features built into the basic R time series packages that everyone ought to know. In this post, I will j...
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How do you know if your model is going to work? Part 4: Cross-validation techniques
by John Mount (more articles) and Nina Zumel (more articles). In this article we conclude our four part series on basic model testing. When fitting and selecting models in a data science project, how do you know that your final model is good? And how sure are you that it's better than the models that you rejected? In this concluding Part 4 of our...
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The R Consortium Gears Up For Business
by Joseph Rickert This week, the Infrastructure Steering Committee (ISC) of the R Consortium unanimously elected Hadley Wickham as its chair thereby also giving Hadley a seat on the R Consortium board of directors. Congratulations Hadley!! This is a major step forward towards putting the R Consortium in business. Not only is the ISC the group th...
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Why Big Data? Learning Curves
by Bob HortonMicrosoft Senior Data Scientist Learning curves are an elaboration of the idea of validating a model on a test set, and have been widely popularized by Andrew Ng’s Machine Learning course on Coursera. Here I present a simple simulation that illustrates this idea. Imagine you use a sample of your data to train a model, then use the...
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R User Groups Highlight R Creativity
by Joseph Rickert I have been a big fan of R user groups since I attended my first meeting. There is just something about the vibe of being around people excited about what they are doing that feels good. From a speaker's perspective, presenting at an R user Group meeting must be the rough equivalent of doing “stand-up” at a club where ...
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Learning R: Index of Online R Courses, October 2015
by Joseph Rickert Early October: somewhere the leaves are turning brilliant colors, temperatures are cooling down and that back to school feeling is in the air. And for more people than ever before, it is going to seem to be a good time to commit to really learning R. I have some suggestions for R courses below, but first: What does it mean to...
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Using miniCRAN in Azure ML
by Michele UsuelliMicrosoft Data Scientist Azure Machine Learning Studio is a drag-and-drop tool to deploy data-driven solutions. It contains pre-built items including data preparation tools and Machine Learning algorithms. In addition, it allows to include R and Python custom scripts. In order to build powerful R tools, you might want to use som...
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The 5th Tribe, Support Vector Machines and caret
by Joseph Rickert In his new book, The Master Algorithm, Pedro Domingos takes on the heroic task of explaining machine learning to a wide audience and classifies machine learning practitioners into 5 tribes*, each with its own fundamental approach to learning problems. To the 5th tribe, the analogizers, Pedro ascribes the Support Vector Machine...
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