Statistical Modeling -- The Full Pragmatic Guide

Continuing Our series of posts on how to interpret Machine Learning algorithms and predictions. Part 0 (optional) -- What is Data Science and the Data Scientist Part 1 -- Introduction to Interpretability Part 1.5 (optional) -- A Brief History of Statistics (May be useful to understand this post) Part 2 -- (this post) Interpreting models of high bias and low variance. Part 4 -- Is it possible to resolve the trade-off between bias and variance? Using Shapley to finally open the black box! In this post we will focus on the interpretation of high bias and low variance models, as we explained in the previous post, these algorithms are the easiest to interpret so assume several prerequisites in the data.