index

What’s New in Tidymodels

Data

Filtered to only include Horror movies

horror_movies |>
  relocate(title, id, runtime, genres) |>
  glimpse()

Rows: 3,759
Columns: 10
$ title                <chr> "Dracula: Dead and Loving It", "From Dusk Till Da…
$ id                   <dbl> 12110, 755, 9102, 9095, 12158, 34996, 8973, 34574…
$ runtime              <dbl> 88, 108, 108, 104, 100, 82, 119, 93, 98, 108, 95,…
$ genres               <chr> "Comedy", "Action, Thriller, Crime", "Science Fic…
$ budget               <dbl> NA, 1.9e+07, 2.0e+07, 4.7e+07, 1.4e+07, NA, NA, N…
$ production_countries <chr> "France, United States of America", "United State…
$ release_date         <date> 1995-12-22, 1996-01-19, 1995-09-08, 1996-02-23, …
$ spoken_languages     <chr> "English, Deutsch", "English, Español", "English"…
$ target               <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, FA…
$ time                 <dbl> 112.804145, 205.761933, 114.304145, 111.904145, 1…

Heavily modified (turns out it is hard to force a themed example)

Multi Dummy Variables

Multi Dummy Variables

both the genres and production_countries variable is formatted as comma-separated lists:

horror_train$genres[1:10]

 [1] "Action, Thriller, Crime"         "Drama, Thriller, Romance"       
 [3] "Drama"                           "Mystery, Thriller"              
 [5] "Science Fiction, Action"         "Thriller, Drama, Mystery"       
 [7] "Drama, Science Fiction, Romance" "Drama"                          
 [9] "Comedy, Thriller"                "Crime, Thriller"

This gives high cardinality

horror_train$genres |> unique() |> length()

[1] 321

despite low number of genres

horror_train$genres |> strsplit(", ") |> unlist() |> unique() |> length()

[1] 19

Survival analysis

What’s New in Tidy model s Emil Hvitfeldt R User Group at the Harvard Data Science Initiative

What’s New in Tidymodels...
What is tidymodels?
Data
Validation splits...
Validation splits
Validation splits
Multi Dummy Variables...
Multi Dummy Variables
Creating dummies
Creating fancy dummies
Creating fancy dummies
Dealing with Dates
New recipes selectors
Final recipe
Sneak peak at data
Survival analysis...
problem: you are...
What do we have
specifying the models
Types of models
Works with workflows
Prediction
Prediction - time
Prediction - linear prediction
Prediction - quantile
Prediction - quantile
Prediction - survival
Prediction - survival
Prediction - hazard
Prediction - hazard
Performance metrics
Performance metrics
Performance metrics
Fitting many models
Clustering...
What is clustering?
What we have
clustering recipe
Peak the data
Specifying models and fitting
Available models
cluster assignment + clusters + prediction
cluster assignment + clusters + prediction
cluster assignment + clusters + prediction
Looking at the clusters
Cluster aware metrics
You can do tuning as well!
Conformal inference...
how to make prediction intervals with no parameteric asssumptions
Calibration...
Post-processing tool
Causal inference
Coming up soon
where to look