Yet another Blood Bowl post! This one is to warm up to the upcoming World Cup, analyzing variation in Tournament Roster choices.

Restate my assumptions: If you graph the numbers of any system, patterns emerge. In this post we'll use clustered heatmaps to graph the numbers from the Blood Bowl Fantasy football game, and see what patterns emerge!

This blog post demonstrates that the presence of irrelevant variables can reduce the performance of the Random Forest algorithm (as implemented in R by `ranger()`). The solution is either to tune one of the algorithm's parameters, OR to remove irrelevant features using a procedure called Recursive Feature Elimination (RFE).

This blog post is on simulating fake data using the R package [simstudy](https://www.rdatagen.net/page/simstudy/). Motivation comes from my interest in converting real datasets into synthetic ones.

In this post, we'll explore the BupaR suite of Process Mining packages created by Gert Janssenswillen of Hasselt University.

In this blog post, I describe the introductory course on Causal Inference I pieced together using various materials available online. It combines Pearl's Causal Graph approach with statistics Gelman/mcElreath style.

In this blog post, we explore how to implement the validation set approach in caret. This is the most basic form of the train/test machine learning concept.

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