It is a subtle point that statistical modeling is different than model based science. However, empirical scientists seem to go out of their way to conflate the two before the public (as statistical modeling is easier to perform and model based science is more highly rewarded). It is often claimed that model based science is being done when in fact statistics is what is being done (for instance some of the unfortunate distractions of flawed reports related to the important question of the magnitude of plausible anthropogenic global warming).
Both model based science and statistics are wonderful fields, but it is important to not receive the results of one when you have paid for the other.
We will pointedly discuss one of the differences. Read more…
The important criterion for a graph is not simply how fast we can see a result; rather it is whether through the use of the graph we can see something that would have been harder to see otherwise or that could not have been seen at all.
– William Cleveland, The Elements of Graphing Data, Chapter 2
In this article, I will discuss some graphs that I find extremely useful in my day-to-day work as a data scientist. While all of them are helpful (to me) for statistical visualization during the analysis process, not all of them will necessarily be useful for presentation of final results, especially to non-technical audiences.
I tend to follow Cleveland’s philosophy, quoted above; these graphs show me — and hopefully you — aspects of data and models that I might not otherwise see. Some of them, however, are non-standard, and tend to require explanation. My purpose here is to share with our readers some ideas for graphical analysis that are either useful to you directly, or will give you some ideas of your own.
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Categories: Applications, Opinion, Statistics, Tutorials Tags: boxplots, ggplot, ggplot2, graphical perception, linear regression, Logistic Regression, R, statistical graphs
We share our admiration for a set of results called “locality sensitive hashing” by demonstrating a greatly simplified example that exhibits the spirit of the techniques. Read more…
Re-read Fred Brooks “The Mythical Man Month” over vacation. Book remains insightful about computer science and project management. Read more…
We give a simple explanation of the interrelated machine learning techniques called kernel methods and support vector machines. We hope to characterize and de-mystify some of the properties of these methods. To do this we work some examples and draw a few analogies. The familiar no matter how wonderful is not perceived as mystical. Read more…
I think I have been pretty productive on technical tasks lately and the method is (at least to me) interesting. The effect was accidental but I think one can explain it and reproduce it by synthesizing three important observations on human behavior. Read more…
Research surveys tend to fall on either end of the spectrum: either they are so high level and cursory in their treatment that they are useful only as a dictionary of terms in the field, or they are so deep and terse that the discussion can only be followed by those already experienced in the field. Ensemble Methods in Data Mining (Seni and Elder, 2010) strikes a good balance between these extremes. This book is an accessible introduction to the theory and practice of ensemble methods in machine learning, with sufficient detail for a novice to begin experimenting right away, and copious references for researchers interested in further details of algorithms and proofs. The treatment focuses on the use of decision trees as base learners (as they are the most common choice), but the principles discussed are applicable with any modeling algorithm. The authors also provide a nice discussion of cross-validation and of the more common regularization techniques.
The heart of the text is the chapter on the Importance Sampling. The authors frame the classic ensemble methods (bagging, boosting, and random forests) as special cases of the Importance Sampling methodology. This not only clarifies the explanations of each approach, but also provides a principled basis for finding improvements to the original algorithms. They have one of the clearest explanations of AdaBoost that I’ve ever read.
A major shortcoming of ensemble methods is the loss of interpretability, when compared to single-model methods such as Decision Trees or Linear Regression. The penultimate chapter is on “Rule Ensembles”: an attempt at a more interpretable ensemble learner. They also discuss measures for variable importance and interaction strength. The last chapter discusses Generalized Degrees of Freedom as an alternative complexity measure and its relationship to potential over-fit.
Overall, I found the book clear and concise, with good attention to practical details. I appreciated the snippets of R code and the references to relevant R packages. One minor nitpick: this book has also been published digitally, presumably with color figures. Because the print version is grayscale, some of the color-coded graphs are now illegible. Usually the major points of the figure are clear from the context in the text; still, the color to grayscale conversion is something for future authors in this series to keep in mind.
Recommended.
A “for fun” piece, reposted from mzlabs.com.
I would like to comment on Duncan Jones’ movie “Moon” and compare some elements of “Moon” to earlier science fiction. Read more…
Stop and think: which of our tools are making us smarter and which of our tools are making us dumber. In my opinion tools and habits that support complexity literally train us to be dumber. Read more…
This is a tutorial on how to try out a new package in R. The summary is: expect errors, search out errors and don’t start with the built in examples or real data.
Suppose you want to try out a novel statistical technique? A good fraction of the time R is your best bet for a first trial. Take as an example general additive models (“Generalized Additive Models,” Trevor J Hastie, Robert Tibshirani, Statistical Science (1986) vol. 1 (3) pp. 297-318); R has a package named “gam” written by Trevor Hastie himself. But, like most R packages, trying the package from the supplied documentation brings in unfamiliar data and concerns. It is best to start small and quickly test if the package itself is suitable to your needs. We give a quick outline of how to learn such a package and quickly find out if the package is for you.
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