1 - Introduction

Introduction to tidymodels

Welcome!

Wi-Fi network name

TODO-ADD-LATER

Wi-Fi password

TODO-ADD-LATER

Who are you?

• You can use the magrittr %>% or base R |> pipe

• You are familiar with functions from dplyr, tidyr, ggplot2

• You have some exposure to basic statistical concepts like linear models and residuals

• You do not need intermediate or expert familiarity with modeling or ML

Who are tidymodels?

• Simon Couch
• Hannah Frick
• Emil Hvitfeldt
• Max Kuhn

Many thanks to Davis Vaughan, Julia Silge, David Robinson, Julie Jung, Alison Hill, and Desirée De Leon for their role in creating these materials!

👀

👀

Plan for this workshop

• Your data budget
• What makes a model
• Evaluating models
• Tuning models

Introduce yourself to your neighbors 👋

What is machine learning?

What is machine learning?

What is machine learning? (2024 edition)

In the early 2010s, “Artificial intelligence” (AI) was largely synonymous with what we’ll refer to as “machine learning” in this workshop. In the late 2010s and early 2020s, AI usually referred to deep learning methods. Since the release of ChatGPT in late 2022, “AI” has come to also encompass large language models / generative models.

What is machine learning?

Your turn

How are statistics and machine learning related?

How are they similar? Different?

03:00

the “two cultures”

model first vs. data first

inference vs. prediction

What is tidymodels?

library(tidymodels)
#> ── Attaching packages ──────────────────────────── tidymodels 1.3.0 ──
#> ✔ broom        1.0.9     ✔ rsample      1.3.1
#> ✔ dials        1.4.1     ✔ tibble       3.3.0
#> ✔ dplyr        1.1.4     ✔ tidyr        1.3.1
#> ✔ infer        1.0.9     ✔ tune         1.3.0
#> ✔ modeldata    1.5.0     ✔ workflows    1.2.0
#> ✔ parsnip      1.3.2     ✔ workflowsets 1.1.1
#> ✔ purrr        1.1.0     ✔ yardstick    1.3.2
#> ✔ recipes      1.3.1
#> ── Conflicts ─────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard() masks scales::discard()
#> ✖ dplyr::filter()  masks stats::filter()
#> ✖ dplyr::lag()     masks stats::lag()
#> ✖ recipes::step()  masks stats::step()

The whole game

• Roadmap for today
• Minimal version of predictive modeling process
• Feature engineering and tuning as iterative extensions

The whole game

The whole game

Stress that we are not fitting a model on the entire training set other than for illustrative purposes in deck 2.

The whole game

The whole game

The whole game

The whole game

The whole game

Let’s install some packages

If you are using your own laptop instead of Posit Cloud:

# Install the packages for the workshop
pkgs <- 
  c("bonsai", "Cubist", "doParallel", "earth", "embed", "finetune", 
    "lightgbm", "lme4", "pak", "parallelly", "plumber", "probably", 
    "ranger", "rpart", "rpart.plot", "rules", "splines2", "stacks", 
    "text2vec", "textrecipes", "tidymodels", "vetiver")

install.packages(pkgs)

pak::pak("simonpcouch/forested")

Our versions

R version 4.5.1 (2025-06-13), Quarto (1.7.33)

package	version
bonsai	0.4.0
broom	1.0.9
Cubist	0.5.0
dials	1.4.1
doParallel	1.0.17
dplyr	1.1.4
earth	5.3.4
embed	1.1.5
finetune	1.2.1
forested	0.1.0.9000
Formula	1.2-5

package	version
ggplot2	3.5.2
lattice	0.22-7
lightgbm	4.6.0
lme4	1.1-37
modeldata	1.5.0
parallelly	1.45.1
parsnip	1.3.2
plotmo	3.6.4
plotrix	3.8-4
plumber	1.3.0
probably	1.1.1

package	version
purrr	1.1.0
ranger	0.17.0
recipes	1.3.1
rpart	4.1.24
rpart.plot	3.1.3
rsample	1.3.1
rules	1.0.2
scales	1.4.0
splines2	0.5.4
stacks	1.1.1
text2vec	0.6.4

package	version
textrecipes	1.1.0
tibble	3.3.0
tidymodels	1.3.0
tidyr	1.3.1
tune	1.3.0
vetiver	0.2.5
workflows	1.2.0
workflowsets	1.1.1
yardstick	1.3.2