Statisticians and data scientists want a neat world where data is arranged in a table such that every row is an observation or instance, and every column is a variable or measurement. Getting to this state of “ready to model format” (often called a denormalized form by relational algebra types) often requires quite a bit of data manipulation. This is how
data.frames describe themselves (try “
str(data.frame(x=1:2))” in an
R-console to see this) and is part of the tidy data manifesto.
SQL (structured query language) and
dplyr can make the data arrangement process less burdensome, but using them effectively requires “index free thinking” where the data are not thought of in terms of row indices. We will explain and motivate this idea below. Continue reading The case for index-free data manipulation
When writing reusable code or packages you often do not know the names of the columns or variables you need to work over. This is what I call “parametric treatment of variables.” This can be a problem when using
R libraries that assume you know the variable names. The
R data manipulation library
dplyr currently supports parametric treatment of variables through “underbar forms” (methods of the form
dplyr::*_), but their use can get tricky.
Rube Goldberg machine 1931 (public domain).
Better support for parametric treatment of variable names would be a boon to
dplyr users. To this end the
replyr package now has a method designed to re-map parametric variable names to known concrete variable names. This allows concrete
dplyr code to be used as if it was parametric. Continue reading Parametric variable names and dplyr
Practical Data Science with R, Zumel, Mount; Manning 2014 is a book Nina Zumel and I are very proud of.
I have written before how I think this book stands out and why you should consider studying from it.
Please read on for a some additional comments on the intent of different sections of the book. Continue reading Teaching Practical Data Science with R
Nina Zumel and I have been doing a lot of writing on the (important) details of re-encoding high cardinality categorical variables for predictive modeling. These are variables that essentially take on string-values (also called levels or factors) and vary through many such levels. Typical examples include zip-codes, vendor IDs, and product codes.
In a sort of “burying the lede” way I feel we may not have sufficiently emphasized that you really do need to perform such re-encodings. Below is a graph (generated in R, code available here) of the kind of disaster you see if you throw such variables into a model without any pre-processing or post-controls.
In the above graph each dot represents the performance of a model fit on synthetic data. The x-axis is model performance (in this case pseudo R-squared, 1 being perfect and below zero worse than using an average). The training pane represents performance on the training data (perfect, but over-fit) and the test pane represents performance on held-out test data (an attempt to simulate future application data). Notice the test performance implies these models are dangerously worse than useless.
Please read on for how to fix this. Continue reading You should re-encode high cardinality categorical variables
Nina Zumel recently mentioned the use of Laplace noise in “count codes” by Misha Bilenko (see here and here) as a known method to break the overfit bias that comes from using the same data to design impact codes and fit a next level model. It is a fascinating method inspired by differential privacy methods, that Nina and I respect but don’t actually use in production.
Nested dolls, Wikimedia Commons
Please read on for my discussion of some of the limitations of the technique, and how we solve the problem for impact coding (also called “effects codes”), and a worked example in R. Continue reading Laplace noising versus simulated out of sample methods (cross frames)
Nina Zumel recently announced upcoming speaking appearances. I want to promote the upcoming sessions at ODSC West 2016 (11:15am-1:00pm on Friday November 4th, or 3:00pm-4:30pm on Saturday November 5th) and invite executives, managers, and other data science consumers to attend. We assume most of the Win-Vector blog audience is made of practitioners (who we hope are already planning to attend), so we are asking you our technical readers to help promote this talk to a broader audience of executives and managers.
Our messages is: if you have to manage data science projects, you need to know how to evaluate results.
In these talks we will lay out how data science results should be examined and evaluated. If you can’t make ODSC (or do attend and like what you see), please reach out to us and we can arrange to present an appropriate targeted summarized version to your executive team. Continue reading Data science for executives and managers
Recently saw a really fun article making the rounds: “The prevalence of statistical reporting errors in psychology (1985–2013)”, Nuijten, M.B., Hartgerink, C.H.J., van Assen, M.A.L.M. et al., Behav Res (2015), doi:10.3758/s13428-015-0664-2. The authors built an R package to check psychology papers for statistical errors. Please read on for how that is possible, some tools, and commentary.
Early automated analysis:
Trial model of a part of the Analytical Engine, built by Babbage, as displayed at the Science Museum (London) (Wikipedia).
Continue reading Proofing statistics in papers
Nina Zumel prepared an excellent article on the consequences of working with relative error distributed quantities (such as wealth, income, sales, and many more) called “Living in A Lognormal World.” The article emphasizes that if you are dealing with such quantities you are already seeing effects of relative error distributions (so it isn’t an exotic idea you bring to analysis, it is a likely fact about the world that comes at you). The article is a good example of how to plot and reason about such situations.
I am just going to add a few additional references (mostly from Nina) and some more discussion on log-normal distributions versus Zipf-style distributions or Pareto distributions. Continue reading Relative error distributions, without the heavy tail theatrics
I just got back from a very good conference organized by startup.ml: Adversarial Machine Learning. Please read on for my to comments on part of one of the very good talks. Continue reading Adversarial machine learning
Writing a book is a sacrifice. It takes a lot of time, represents a lot of missed opportunities, and does not (directly) pay very well. If you do a good job it may pay back in good-will, but producing a serious book is a great challenge.
Nina Zumel and I definitely troubled over possibilities for some time before deciding to write Practical Data Science with R, Nina Zumel, John Mount, Manning 2014.
In the end we worked very hard to organize and share a lot of good material in what we feel is a very readable manner. But I think the first-author may have been signaling and preparing a bit earlier than I was aware we were writing a book. Please read on to see some of her prefiguring work. Continue reading Did she know we were writing a book?