There is much ado about variable selection or variable utility valuation in supervised machine learning. In this note we will try to disarm some possibly common fallacies, and to set reasonable expectations about how variable valuation can work. Introduction In general variable valuation is estimating the utility that a column […]

Estimated reading time: 14 minutes

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

If you are working with predictive modeling or machine learning in R this is the R tip that is going to save you the most time and deliver the biggest improvement in your results. R Tip: Use the vtreat package for data preparation in predictive analytics and machine learning projects. […]

Estimated reading time: 2 minutes

[Reader’s Note. Some of our articles are applied and some of our articles are more theoretical. The following article is more theoretical, and requires fairly formal notation to even work through. However, it should be of interest as it touches on some of the fine points of cross-validation that are […]

Estimated reading time: 3 minutes

Introduction Suppose we have the task of predicting an outcome y given a number of variables v1,..,vk. We often want to “prune variables” or build models with fewer than all the variables. This can be to speed up modeling, decrease the cost of producing future data, improve robustness, improve explain-ability, […]

Estimated reading time: 15 minutes