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.
In this post, I show how to create a Arduino-based atmospheric sensor prototype capable of storing large amounts of data on a microSD card.
In this post, we test both Bayesian Additive Regression Trees (BART)
and Causal forests (grf)
on four simulated datasets of increasing complexity. May the best method win!
In this post, I explore how we can improve a parametric regression model by comparing its predictions to those of a Random Forest model. This might informs us in what ways the OLS model fails to capture all non-linearities and interactions between the predictors.