👋 Who am I?

👋 Who are we?

👋 Who are you?

Roadmap

What is tidymodels?
Why tidymodels?
Applied example🛩
Resources

github.com/simonpcouch/tidymodels-uw-2023

What is tidymodels?

The tidymodels framework is a collection of packages for modeling and machine learning using tidyverse principles.

- tidymodels.org

…so what is modeling and machine learning?

BYO Venn Diagram

The tidymodels framework is a collection of packages for safe, performant, and expressive supervised predictive modeling on tabular data.

🥴

The tidymodels framework is a collection of packages for safe, performant, and expressive supervised predictive modeling on tabular data.

🥴

The tidymodels framework is a collection of packages for safe, performant, and expressive supervised predictive modeling on tabular data.

🥴

The tidymodels framework is a collection of packages for safe, performant, and expressive supervised predictive modeling on tabular data.

🥴

The tidymodels framework is a collection of packages for safe, performant, and expressive supervised predictive modeling on tabular data.

🥴

Think about the modeling problem, not the syntax.

Why tidymodels?

Why tidymodels? Consistency

How many different ways can you think of to fit a linear model in R?

The blessing:

Many statistical modeling practitioners implement methods in R

The curse:

Many statistical modeling practitioners implement methods in R

Why tidymodels? Consistency

mtcars
#>                      mpg cyl  disp  hp drat    wt  qsec vs am gear
#> Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4
#> Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4
#> Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4
#> Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3
#> Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3
#> Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3
#> Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3
#> Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4
#> Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4
#> Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4
#> Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4
#> Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3
#> Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3
#> Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3
#> Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3
#> Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3
#> Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3
#> Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4
#> Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4
#> Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4
#> Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3
#> Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3
#> AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3
#> Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3
#> Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3
#> Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4
#> Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5
#> Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5
#> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5
#> Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5
#> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5
#> Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4
#>                     carb
#> Mazda RX4              4
#> Mazda RX4 Wag          4
#> Datsun 710             1
#> Hornet 4 Drive         1
#> Hornet Sportabout      2
#> Valiant                1
#> Duster 360             4
#> Merc 240D              2
#> Merc 230               2
#> Merc 280               4
#> Merc 280C              4
#> Merc 450SE             3
#> Merc 450SL             3
#> Merc 450SLC            3
#> Cadillac Fleetwood     4
#> Lincoln Continental    4
#> Chrysler Imperial      4
#> Fiat 128               1
#> Honda Civic            2
#> Toyota Corolla         1
#> Toyota Corona          1
#> Dodge Challenger       2
#> AMC Javelin            2
#> Camaro Z28             4
#> Pontiac Firebird       2
#> Fiat X1-9              1
#> Porsche 914-2          2
#> Lotus Europa           2
#> Ford Pantera L         4
#> Ferrari Dino           6
#> Maserati Bora          8
#> Volvo 142E             2

Why tidymodels? Consistency

With lm():

model <- 
  lm(mpg ~ ., mtcars)

With tidymodels:

model <-
  linear_reg() %>%
  set_engine("lm") %>%
  fit(mpg ~ ., mtcars)

Why tidymodels? Consistency

With glmnet:

model <- 
  glmnet(
    as.matrix(mtcars[2:11]),
    mtcars$mpg
  )

With tidymodels:

model <-
  linear_reg() %>%
  set_engine("glmnet") %>%
  fit(mpg ~ ., mtcars)

Why tidymodels? Consistency

With h2o:

h2o.init()
as.h2o(mtcars, "cars")

model <- 
  h2o.glm(
    x = colnames(mtcars[2:11]), 
    y = "mpg",
    "cars"
  )

With tidymodels:

model <-
  linear_reg() %>%
  set_engine("h2o") %>%
  fit(mpg ~ ., mtcars)

Why tidymodels? Consistency

Why tidymodels? Safety¹

Overfitting leads to analysts believing models are more performant than they actually are.

A 2023 review found data leakage to be “a widespread failure mode in machine-learning (ML)-based science.”

Implementations of the same machine learning model give differing results, resulting in irreproducibility of modeling results.

Why tidymodels? Safety

Why tidymodels? Completeness

Built-in support for 99 machine learning models!

#> # A tibble: 99 × 2
#>    name       engine   
#>    <chr>      <chr>    
#>  1 boost_tree C5.0     
#>  2 boost_tree h2o      
#>  3 boost_tree h2o_gbm  
#>  4 boost_tree lightgbm 
#>  5 boost_tree mboost   
#>  6 boost_tree spark    
#>  7 boost_tree xgboost  
#>  8 null_model parsnip  
#>  9 svm_linear LiblineaR
#> 10 svm_linear kernlab  
#> # ℹ 89 more rows

Why tidymodels? Completeness

Built-in support for 102 data pre-processing techniques!

#> # A tibble: 102 × 1
#>    name               
#>    <chr>              
#>  1 step_rename_at     
#>  2 step_scale         
#>  3 step_kpca          
#>  4 step_percentile    
#>  5 step_depth         
#>  6 step_poly_bernstein
#>  7 step_impute_linear 
#>  8 step_novel         
#>  9 step_nnmf_sparse   
#> 10 step_slice         
#> # ℹ 92 more rows

Why tidymodels? Extensibility

Can’t find the technique you need?

Why tidymodels? Extensibility

Why tidymodels? Deployability

Tightly integrated with Posit Workbench and Connect

Workbench: scalable, on-demand computational resources
Connect: share work with collaborators and practitioners

Applied example🛩

Who all is flying home for the holidays?

😬🎅😬🎅😬☃️😬🎅😬☃️😬🎁😬

Flights out of Madison, WI

anyflights("MSN", 2022)

#> # A tibble: 10,754 × 14
#>   delayed airline     flight origin destination date        hour plane
#>   <fct>   <chr>       <fct>  <fct>  <fct>       <date>     <dbl> <fct>
#> 1 No      Endeavor A… 51     MSN    ATL         2022-01-01     5 N901…
#> 2 No      PSA Airlin… 59     MSN    CLT         2022-01-01     6 N570…
#> 3 No      Envoy Air   27     MSN    MIA         2022-01-01     6 N280…
#> 4 No      American A… 3      MSN    PHX         2022-01-01     6 N662…
#> 5 Yes     American A… 16     MSN    DFW         2022-01-01     7 N826…
#> 6 No      SkyWest Ai… 26     MSN    MSP         2022-01-01     7 N282…
#> # ℹ 10,748 more rows
#> # ℹ 6 more variables: distance <dbl>, duration <dbl>,
#> #   wind_speed <dbl>, precip <dbl>, visibility <dbl>,
#> #   plane_year <int>

Note

See /example/query_data.R for the actual code to generate this data!

Flights out of Madison, WI

Around 17% of flights are delayed.

Flights out of Madison, WI

Round 1: evaluate unique combinations of

Preprocessors:

formula (delayed ~ .)
center and scale predictors
PCA + center and scale predictors

…and models:

logistic regression
bagged MARS
bagged decision tree
neural network
support vector machine
LightGBM boosted tree

Flights out of Madison, WI

Round 2: investigate and refine whatever works!

Flights out of Madison, WI

See how we carried that analysis out here:

github.com/simonpcouch/tidymodels-uw-2023

Resources

tidyverse: r4ds.hadley.nz

Resources

tidyverse: r4ds.hadley.nz
tidymodels: tmwr.org

Resources

tidyverse: r4ds.hadley.nz
tidymodels: tmwr.org
Posit Team: posit.co/team

Resources

tidyverse: r4ds.hadley.nz
tidymodels: tmwr.org
Posit Team: posit.co/team
Slides and example notebook:

github.com/simonpcouch/tidymodels-uw-2023

Thank you!

👋 Who am I?

👋 Who are we?

👋 Who are you?

Roadmap

What is tidymodels?

BYO Venn Diagram

Why tidymodels?

Why tidymodels? Consistency

Why tidymodels? Consistency

Why tidymodels? Consistency

Why tidymodels? Consistency

Why tidymodels? Consistency

Why tidymodels? Consistency

Why tidymodels? Safety1

Why tidymodels? Safety1

Why tidymodels? Safety

Why tidymodels? Completeness

Why tidymodels? Completeness

Why tidymodels? Completeness

Why tidymodels? Extensibility

Why tidymodels? Extensibility

Why tidymodels? Deployability

Applied example🛩

Who all is flying home for the holidays?

😬🎅😬🎅😬☃️😬🎅😬☃️😬🎁😬

Flights out of Madison, WI

Flights out of Madison, WI

Flights out of Madison, WI

Flights out of Madison, WI

Flights out of Madison, WI

Flights out of Madison, WI

Flights out of Madison, WI

Flights out of Madison, WI

Flights out of Madison, WI

Resources

Resources

Resources

Resources

Resources

Why tidymodels? Safety¹

Why tidymodels? Safety¹