This a machine-learning-vs-traditional-statistics kind of blog post inspired by Leo Breiman's "Statistical Modeling: The Two Cultures". If you're like: "I had enough of this machine learning vs. statistics discussion, BUT I would love to see beautiful beamer-slides with an awesome font.", then jump to the bottom of the post and for my slides on this subject plus source code.

I prepared presentation slides about the paper for a university course. Leo Breiman basically argued, that there are two cultures of statistical modeling:

Data modeling culture: You assume to know the underlying data-generating process and model your data accordingly. For example if you choose to model your data with a linear regression model you assume that the outcome y is normally distributed given the covariates x. This is a typical procedure in traditional statistics. Algorithmic modeling culture: You treat the true data-generating process as unkown and try to find a model that is…

I prepared presentation slides about the paper for a university course. Leo Breiman basically argued, that there are two cultures of statistical modeling:

Data modeling culture: You assume to know the underlying data-generating process and model your data accordingly. For example if you choose to model your data with a linear regression model you assume that the outcome y is normally distributed given the covariates x. This is a typical procedure in traditional statistics. Algorithmic modeling culture: You treat the true data-generating process as unkown and try to find a model that is…