I am working on a promising new series of notes: common data science fallacies and pitfalls. (Probably still looking for a good name for the series!) I thought I would share a few thoughts on it, and hopefully not jinx it too badly.

Estimated reading time: 4 minutes

A common mis-understanding of linear regression and logistic regression is that the intercept is thought to encode the unconditional mean or the training data prevalence. This is easily seen to not be the case. Consider the following example in R. library(wrapr) We set up our example data. # build our […]

Estimated reading time: 1 minute

We have a new R WVPlots plot: ROCPlotPairList. It is useful for comparing the ROC/AUC of multiple models on the same data set. library(WVPlots) set.seed(34903490) x1 <- rnorm(50) x2 <- rnorm(length(x1)) x3 <- rnorm(length(x1)) y <- 0.2*x2^2 + 0.5*x2 + x1 + rnorm(length(x1)) frm <- data.frame( x1 = x1, x2 […]

Estimated reading time: 47 seconds

I’ve added a worked R example of the non-convexity, with respect to model parameters, of square loss of a sigmoid-derived prediction here. This is finishing an example for our Python note “Why not Square Error for Classification?”. Reading that note will give a usable context and background for this diagram. […]

Estimated reading time: 59 seconds

In our data science teaching, we present the ROC plot (and the area under the curve of the plot, or AUC) as a useful tool for evaluating score-based classifier models, as well as for comparing multiple such models. The ROC is informative and useful, but it’s also perhaps overly concise […]

Estimated reading time: 12 minutes

I would like to re-share links to our free vtreat data preparation system introduction videos, which show you what sort of machine learning problems vtreat can help you with. Python vtreat introduction video (PyData LA 2019), slides here. R vtreat introduction video (Why R? Foundation). The idea is: instead of […]

Estimated reading time: 58 seconds

Chapter 8 “Advanced Data Preparation” of Practical Data Science with R is a study in: Using the R vtreat package for advanced data preparation. Cross-validated data preparation. It is the professionally edited, ready to cite version of an important data preparation methodology. An advantage being: a number of well documented […]

Estimated reading time: 59 seconds

One of the chapters that we are especially proud of in Practical Data Science with R is Chapter 7, “Linear and Logistic Regression.” We worked really hard to explain the fundamental principles behind both methods in a clear and easy-to-understand form, and to document diagnostics returned by the R implementations […]

Estimated reading time: 52 seconds

We have an exciting new article to share: Don’t Feel Guilty About Selecting Variables. If you are at all interested in the probabilistic justification of important data science techniques, such as variable selection or pruning, this should be an informative and fun read. “Data Science” is often criticized with the […]

Estimated reading time: 1 minute