lr_mod <- logistic_reg() |>
set_engine(engine = "glm") |>
set_mode("classification")
lr_modLogistic Regression Model Specification (classification)
Computational engine: glm Predicting children in hotel bookings
Suggested answers
Application exercise
Answers
Your Turn 1
Run the chunk below and look at the output. Then, copy/paste the code and edit to create:
a decision tree model for classification
that uses the
C5.0engine.
Save it as tree_mod and look at the object. What is different about the output?
Hint: you’ll need https://www.tidymodels.org/find/parsnip/
tree_mod <- decision_tree() |>
set_engine(engine = "C5.0") |>
set_mode("classification")
tree_mod Decision Tree Model Specification (classification)
Computational engine: C5.0 Your Turn 2
Fill in the blanks.
Use initial_split(), training(), and testing() to:
Split hotels into training and test sets. Save the rsplit!
Extract the training data and fit your classification tree model.
Check the proportions of the
testvariable in each set.
Keep set.seed(100) at the start of your code.
Hint: Be sure to remove every _ before running the code!
set.seed(100) # Important!
hotels_split <- initial_split(data = hotels, prop = 3/4)
hotels_train <- training(hotels_split)
hotels_test <- testing(hotels_split)
# check distribution
count(x = hotels_train, children) |>
mutate(prop = n / sum(n))# A tibble: 2 × 3
children n prop
<fct> <int> <dbl>
1 children 1503 0.501
2 none 1497 0.499count(x = hotels_test, children) |>
mutate(prop = n / sum(n))# A tibble: 2 × 3
children n prop
<fct> <int> <dbl>
1 children 497 0.497
2 none 503 0.503Your Turn 3
Run the code below. What does it return?
set.seed(100)
hotels_folds <- vfold_cv(data = hotels_train, v = 10)
hotels_folds# 10-fold cross-validation
# A tibble: 10 × 2
splits id
<list> <chr>
1 <split [2700/300]> Fold01
2 <split [2700/300]> Fold02
3 <split [2700/300]> Fold03
4 <split [2700/300]> Fold04
5 <split [2700/300]> Fold05
6 <split [2700/300]> Fold06
7 <split [2700/300]> Fold07
8 <split [2700/300]> Fold08
9 <split [2700/300]> Fold09
10 <split [2700/300]> Fold10Your Turn 4
Add a autoplot() to visualize the ROC AUC.
tree_preds <- tree_mod |>
fit_resamples(
children ~ average_daily_rate + stays_in_weekend_nights,
resamples = hotels_folds,
control = control_resamples(save_pred = TRUE)
)
tree_preds |>
collect_predictions() |>
roc_curve(truth = children, .pred_children) |>
autoplot()
Acknowledgments
- Materials derived from Tidymodels, Virtually: An Introduction to Machine Learning with Tidymodels by Allison Hill.
- Dataset and some modeling steps derived from A predictive modeling case study and licensed under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA) License.
Session information
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.3.1 (2023-06-16)
os macOS Ventura 13.5.2
system aarch64, darwin20
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2023-11-06
pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
backports 1.4.1 2021-12-13 [1] CRAN (R 4.3.0)
bit 4.0.5 2022-11-15 [1] CRAN (R 4.3.0)
bit64 4.0.5 2020-08-30 [1] CRAN (R 4.3.0)
broom * 1.0.5 2023-06-09 [1] CRAN (R 4.3.0)
C50 * 0.1.8 2023-02-08 [1] CRAN (R 4.3.0)
class 7.3-22 2023-05-03 [1] CRAN (R 4.3.0)
cli 3.6.1 2023-03-23 [1] CRAN (R 4.3.0)
codetools 0.2-19 2023-02-01 [1] CRAN (R 4.3.0)
colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.3.0)
crayon 1.5.2 2022-09-29 [1] CRAN (R 4.3.0)
Cubist 0.4.2.1 2023-03-09 [1] CRAN (R 4.3.0)
data.table 1.14.8 2023-02-17 [1] CRAN (R 4.3.0)
dials * 1.2.0 2023-04-03 [1] CRAN (R 4.3.0)
DiceDesign 1.9 2021-02-13 [1] CRAN (R 4.3.0)
digest 0.6.33 2023-07-07 [1] CRAN (R 4.3.0)
dplyr * 1.1.3 2023-09-03 [1] CRAN (R 4.3.0)
evaluate 0.22 2023-09-29 [1] CRAN (R 4.3.1)
fansi 1.0.5 2023-10-08 [1] CRAN (R 4.3.1)
farver 2.1.1 2022-07-06 [1] CRAN (R 4.3.0)
fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.3.0)
forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.3.0)
foreach 1.5.2 2022-02-02 [1] CRAN (R 4.3.0)
Formula 1.2-5 2023-02-24 [1] CRAN (R 4.3.0)
furrr 0.3.1 2022-08-15 [1] CRAN (R 4.3.0)
future 1.32.0 2023-03-07 [1] CRAN (R 4.3.0)
future.apply 1.11.0 2023-05-21 [1] CRAN (R 4.3.0)
generics 0.1.3 2022-07-05 [1] CRAN (R 4.3.0)
ggplot2 * 3.4.2 2023-04-03 [1] CRAN (R 4.3.0)
globals 0.16.2 2022-11-21 [1] CRAN (R 4.3.0)
glue 1.6.2 2022-02-24 [1] CRAN (R 4.3.0)
gower 1.0.1 2022-12-22 [1] CRAN (R 4.3.0)
GPfit 1.0-8 2019-02-08 [1] CRAN (R 4.3.0)
gtable 0.3.3 2023-03-21 [1] CRAN (R 4.3.0)
hardhat 1.3.0 2023-03-30 [1] CRAN (R 4.3.0)
here 1.0.1 2020-12-13 [1] CRAN (R 4.3.0)
hms 1.1.3 2023-03-21 [1] CRAN (R 4.3.0)
htmltools 0.5.6.1 2023-10-06 [1] CRAN (R 4.3.1)
htmlwidgets 1.6.2 2023-03-17 [1] CRAN (R 4.3.0)
infer * 1.0.4 2022-12-02 [1] CRAN (R 4.3.0)
inum 1.0-5 2023-03-09 [1] CRAN (R 4.3.0)
ipred 0.9-14 2023-03-09 [1] CRAN (R 4.3.0)
iterators 1.0.14 2022-02-05 [1] CRAN (R 4.3.0)
jsonlite 1.8.7 2023-06-29 [1] CRAN (R 4.3.0)
knitr 1.44 2023-09-11 [1] CRAN (R 4.3.0)
labeling 0.4.2 2020-10-20 [1] CRAN (R 4.3.0)
lattice 0.21-8 2023-04-05 [1] CRAN (R 4.3.0)
lava 1.7.2.1 2023-02-27 [1] CRAN (R 4.3.0)
lhs 1.1.6 2022-12-17 [1] CRAN (R 4.3.0)
libcoin 1.0-9 2021-09-27 [1] CRAN (R 4.3.0)
lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.3.0)
listenv 0.9.0 2022-12-16 [1] CRAN (R 4.3.0)
lubridate * 1.9.2 2023-02-10 [1] CRAN (R 4.3.0)
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.3.0)
MASS 7.3-60 2023-05-04 [1] CRAN (R 4.3.0)
Matrix 1.5-4.1 2023-05-18 [1] CRAN (R 4.3.0)
modeldata * 1.1.0 2023-01-25 [1] CRAN (R 4.3.0)
modelenv 0.1.1 2023-03-08 [1] CRAN (R 4.3.0)
munsell 0.5.0 2018-06-12 [1] CRAN (R 4.3.0)
mvtnorm 1.2-2 2023-06-08 [1] CRAN (R 4.3.0)
nnet 7.3-19 2023-05-03 [1] CRAN (R 4.3.0)
parallelly 1.36.0 2023-05-26 [1] CRAN (R 4.3.0)
parsnip * 1.1.0 2023-04-12 [1] CRAN (R 4.3.0)
partykit 1.2-20 2023-04-14 [1] CRAN (R 4.3.0)
pillar 1.9.0 2023-03-22 [1] CRAN (R 4.3.0)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.3.0)
plyr 1.8.8 2022-11-11 [1] CRAN (R 4.3.0)
prodlim 2023.03.31 2023-04-02 [1] CRAN (R 4.3.0)
purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.3.0)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.3.0)
Rcpp 1.0.10 2023-01-22 [1] CRAN (R 4.3.0)
readr * 2.1.4 2023-02-10 [1] CRAN (R 4.3.0)
recipes * 1.0.6 2023-04-25 [1] CRAN (R 4.3.0)
reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.3.0)
rlang 1.1.1 2023-04-28 [1] CRAN (R 4.3.0)
rmarkdown 2.25 2023-09-18 [1] CRAN (R 4.3.1)
rpart 4.1.19 2022-10-21 [1] CRAN (R 4.3.0)
rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.3.0)
rsample * 1.1.1 2022-12-07 [1] CRAN (R 4.3.0)
rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.3.0)
scales * 1.2.1 2022-08-20 [1] CRAN (R 4.3.0)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.3.0)
stringi 1.7.12 2023-01-11 [1] CRAN (R 4.3.0)
stringr * 1.5.0 2022-12-02 [1] CRAN (R 4.3.0)
survival 3.5-5 2023-03-12 [1] CRAN (R 4.3.0)
tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.3.0)
tidymodels * 1.1.0 2023-05-01 [1] CRAN (R 4.3.0)
tidyr * 1.3.0 2023-01-24 [1] CRAN (R 4.3.0)
tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.3.0)
tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.3.0)
timechange 0.2.0 2023-01-11 [1] CRAN (R 4.3.0)
timeDate 4022.108 2023-01-07 [1] CRAN (R 4.3.0)
tune * 1.1.1 2023-04-11 [1] CRAN (R 4.3.0)
tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.3.0)
utf8 1.2.3 2023-01-31 [1] CRAN (R 4.3.0)
vctrs 0.6.4 2023-10-12 [1] CRAN (R 4.3.1)
vroom 1.6.3 2023-04-28 [1] CRAN (R 4.3.0)
withr 2.5.1 2023-09-26 [1] CRAN (R 4.3.1)
workflows * 1.1.3 2023-02-22 [1] CRAN (R 4.3.0)
workflowsets * 1.0.1 2023-04-06 [1] CRAN (R 4.3.0)
xfun 0.40 2023-08-09 [1] CRAN (R 4.3.0)
yaml 2.3.7 2023-01-23 [1] CRAN (R 4.3.0)
yardstick * 1.2.0 2023-04-21 [1] CRAN (R 4.3.0)
[1] /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library
──────────────────────────────────────────────────────────────────────────────