1 - Introduction

Machine learning with tidymodels

Welcome!

Wi-Fi network name

tktk

Wi-Fi password

tktk

Who are you?

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

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

• You have exposure to basic statistical concepts

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

Who am I?

@juliasilge

@juliasilge

youtube.com/juliasilge

juliasilge.com

Many thanks to RStudio tidymodels team, Alison Hill, and Allison Horst for their role in creating these materials!

Asking for help

🟪 “I’m stuck and need help!”

🟩 “I finished the exercise”

Plan for this workshop

• Your data budget
• What makes a model
• Evaluating models
• Feature engineering
• Tuning hyperparameters
• Wrapping up!

Introduce yourself to your neighbors 👋

What is machine learning?

What is machine learning?

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 1.0.0 ──
#> ✔ broom        1.0.0     ✔ rsample      1.0.0
#> ✔ dials        1.0.0     ✔ tibble       3.1.8
#> ✔ dplyr        1.0.9     ✔ tidyr        1.2.0
#> ✔ infer        1.0.2     ✔ tune         1.0.0
#> ✔ modeldata    1.0.0     ✔ workflows    1.0.0
#> ✔ parsnip      1.0.0     ✔ workflowsets 1.0.0
#> ✔ purrr        0.3.4     ✔ yardstick    1.0.0
#> ✔ recipes      1.0.1
#> ── Conflicts ─────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard() masks scales::discard()
#> ✖ dplyr::filter()  masks stats::filter()
#> ✖ dplyr::lag()     masks stats::lag()
#> ✖ recipes::step()  masks stats::step()
#> • Use tidymodels_prefer() to resolve common conflicts.

Let’s install some packages

install.packages(c("doParallel", "ranger", "rpart", 
                   "rpart.plot", "tidymodels", "tidyverse",
                   "vetiver", "xgboost"))