Blog Archives

vtreat cross frames

May 5, 2016
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vtreat cross frames

vtreat cross frames John Mount, Nina Zumel 2016-05-05 As a follow on to “On Nested Models” we work R examples demonstrating “cross validated training frames” (or “cross frames”) in vtreat. Consider the following data frame. The outcome only depends on the “good” variables, not on the (high degree of freedom) “bad” variables. Modeling such a … Continue reading...

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On Nested Models

April 26, 2016
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On Nested Models

We have been recently working on and presenting on nested modeling issues. These are situations where the output of one trained machine learning model is part of the input of a later model or procedure. I am now of the opinion that correct treatment of nested models is one of the biggest opportunities for improvement … Continue reading...

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Improved vtreat documentation

April 17, 2016
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Improved vtreat documentation

Nina Zumel has donated some time to greatly improve the vtreat R package documentation (now available as pre-rendered HTML here). vtreat is an R data.frame processor/conditioner package that helps prepare real-world data for predictive modeling in a statistically justifiable manner. Even with modern machine learning techniques (random forests, support vector machines, neural nets, gradient boosted … Continue reading...

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Free data science video lecture: debugging in R

April 9, 2016
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We are pleased to release a new free data science video lecture: Debugging R code using R, RStudio and wrapper functions. In this 8 minute video we demonstrate the incredible power of R using wrapper functions to catch errors for later reproduction and debugging. If you haven’t tried these techniques this will really improve your … Continue reading...

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Half off Win-Vector data science books and video training!

April 8, 2016
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We are pleased to announce our book Practical Data Science with R (Nina Zumel, John Mount, Manning 2014) is part of Manning’s “Deal of the Day” of April 9th 2016. This one day only offer gets you half off for physical book (with free e-copy) or paid e-copy (e-copy simultaneous pdf + ePub + kindle, … Continue reading...

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A bit on the F1 score floor

April 2, 2016
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A bit on the F1 score floor

At Strata+Hadoop World “R Day” Tutorial, Tuesday, March 29 2016, San Jose, California we spent some time on classifier measures derived from the so-called “confusion matrix.” We repeated our usual admonition to not use “accuracy” as a project goal (business people tend to ask for it as it is the word they are most familiar … Continue reading...

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WVPlots: example plots in R using ggplot2

April 1, 2016
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WVPlots: example plots in R using ggplot2

Nina Zumel and I have been working on packaging our favorite graphing techniques in a more reusable way that emphasizes the analysis task at hand over the steps needed to produce a good visualization. The idea is: we sacrifice some of the flexibility and composability inherent to ggplot2 in R for a menu of prescribed … Continue reading...

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For loops in R can lose class information

March 24, 2016
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Did you know R‘s for() loop control structure drops class annotations from vectors? Consider the following code R code demonstrating three uses of a for-loop that one would expect to behave very similarly. dates <- c(as.Date('2015-01-01'),as.Date('2015-01-02')) for(ii in seq_along(dates)) { di <- dates print(di) } ## "2015-01-01" ## "2015-01-02" for(di in as.list(dates)) { … Continue reading...

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Upcoming Win-Vector LLC appearances

March 23, 2016
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Upcoming Win-Vector LLC appearances

Win-Vector LLC will be presenting on statistically validating models using R and data science at: Strata+Hadoop World “R Day” Tutorial 9:00am–5:00pm Tuesday, March 29 2016, San Jose, California. ODSC San Francisco Meetup, 6:30pm-9:00pm Thursday, March 31, 2016, San Francisco, California. We will share code and examples. Registration required (and Strata is a paid conference). Please … Continue reading...

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sample(): “Monkey’s Paw” style programming in R

March 22, 2016
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sample(): “Monkey’s Paw” style programming in R

The R functions base::sample and base::sample.int are functions that include extra “conveniences” that seem to have no purpose beyond encouraging grave errors. In this note we will outline the problem and a suggested work around. Obviously the R developers are highly skilled people with good intent, and likely have no choice in these matters (due … Continue reading...

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