Tidy Modeling with R Book Club
Welcome
- Book club meetings
1 Software for modeling
2 A tidyverse primer
3 A review of R modeling fundamentals
Basics
4 The Ames housing data
- 4.1 Pittsburgh: a parallel real world example
- 4.2 Meeting Videos
5 Spending our data
6 Fitting models with parsnip
AI Ethics
- 6.7 Meeting Videos
  - 6.7.1 Cohort 2
7 A model workflow
8 Feature engineering with recipes
9 Judging model effectiveness
Review of chapters 4-9
- 9.10 Meeting Videos
  - 9.10.1 Cohort 1
Tools for Creating Effective Models
10 Resampling for evaluating performance
11 Comparing models with resampling
12 Model tuning and the dangers of overfitting
13 Grid search
14 Iterative search
15 Screening Many Models
Review of chapters 10-15
- 15.6 Meeting Videos
  - 15.6.1 Cohort 1
16 Dimensionality reduction
Other Topics
17 Encoding categorical data
- 17.1 Effect of Encoding
- 17.2 Encoding methods:
  - 17.2.1 Cohort 4
18 Explaining models and predictions
19 When should you trust predictions?
20 Ensembles of models
21 Inferential analysis
Published with bookdown

Tidy Modeling with R Book Club

Review of chapters 4-9

For this week, Cohort 1 met with the authors, Max Kuhn and Julia Silge!