Here is what I'm thinking about in terms of structure:

(* means maybe we can discuss this?)

• Chapter: Characterizing Classification Models
• Chapter: Linear and Additive Classifiers
  • Intro Example: forestation
    • EDA
    • Feature engineering
  • Logistic regression
    • Maximum likelihood
    • Regularized
    • Bayesian estimation*
  • Multinomial regression
  • GAMs
  • LDA*
  • Naive Bayes
• Chapter: Complex Nonlinear Boundaries
  • Nonlinear discriminant analysis
    • MDA
    • FDA
    • DANN*
  • Neural networks
    • Single layer, Feedforward
    • TabPFN
  • KNN
  • SVMs
• Chapter: Classification using Trees and Rule
  • Elements of trees
    • Splitting
    • Growing
    • Pruning
    • Missing data handling
  • Single Trees
    • CART
    • C5.0
    • Conditional Inference
    • Oblique
  • Bagging
  • Random Forest
  • BART
  • Boosting
  • Rules
    • RuleFit
    • C5.0
• Chapter: Classification summary
  • Final forested results
  • Other things:
    • Ordinal outcomes
    • Multilabel