4 Classification

Categorical or qualitative outcome variables are ubiquitous. We review some supervised learning methods for classification, and see how these may be applied to observational causal inference.

Joshua Loftus


Link Type Description
html pdf Slides Classification and logistic regression
[html] Rmd Notebook Classification, class balance, ROC curves
pdf html Rmd Notebook Gradient descent and numeric differentiation
html Exercises First exercise set

To be updated


Required reading

Supplemental reading

Carving nature at its joints

“A good cook gets a new knife every year; he chops! Mediocre cooks change knives monthly; they hack. My knife now has 19 years on it; it’s carved several thousand oxen and the edge is as if I had just taken it from the sharpener. Those joints have gaps, and the knife’s edge no thickness, to put something infinitesimally thin in an empty space?! Effortless! It even allows the edge wander in with ample room to play. That is why, with 19 years on it, this knife’s edge is grindstone fresh.” - Butcher Ding, the Zhuangzi


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For attribution, please cite this work as

Loftus (2021, Oct. 8). machine learning 4 data science: 4 Classification. Retrieved from http://ml4ds.com/weeks/04-classification/

BibTeX citation

  author = {Loftus, Joshua},
  title = {machine learning 4 data science: 4 Classification},
  url = {http://ml4ds.com/weeks/04-classification/},
  year = {2021}