3 - What makes a model?

Machine learning with tidymodels

Your turn

How do you fit a linear model in R?

How many different ways can you think of?

03:00

• lm for linear model

• glm for generalized linear model (e.g. logistic regression)

• glmnet for regularized regression

• keras for regression using TensorFlow

• stan for Bayesian regression

• spark for large data sets

To specify a model

• Choose a model
• Specify an engine
• Set the mode

To specify a model

linear_reg()
#> Linear Regression Model Specification (regression)
#> 
#> Computational engine: lm

Models have default engines

To specify a model

• Choose a model
• Specify an engine
• Set the mode

To specify a model

linear_reg() %>%
  set_engine("glmnet")
#> Linear Regression Model Specification (regression)
#> 
#> Computational engine: glmnet

To specify a model

linear_reg() %>%
  set_engine("stan")
#> Linear Regression Model Specification (regression)
#> 
#> Computational engine: stan

To specify a model

• Choose a model
• Specify an engine
• Set the mode

To specify a model

decision_tree()
#> Decision Tree Model Specification (unknown mode)
#> 
#> Computational engine: rpart

Some models have a default mode

To specify a model

decision_tree() %>% 
  set_mode("regression")
#> Decision Tree Model Specification (regression)
#> 
#> Computational engine: rpart

All available models are listed at https://www.tidymodels.org/find/parsnip/

To specify a model

• Choose a model
• Specify an engine
• Set the mode

Your turn

Run the tree_spec chunk in your .qmd.

Edit this code so it creates a different model.

05:00

All available models are listed at https://www.tidymodels.org/find/parsnip/

Models we’ll be using today

• Linear regression
• Decision trees

Linear regression

Linear regression

Linear regression

• Outcome modeled as linear combination of predictors:

\(\mbox{latency} = \beta_0 + \beta_1\cdot\mbox{age} + \epsilon\)

• Find a line that minimizes the mean squared error (MSE)

Decision trees

Decision trees

• Series of splits or if/then statements based on predictors

• First the tree grows until some condition is met (maximum depth, no more data)

• Then the tree is pruned to reduce its complexity

Decision trees

All models are wrong, but some are useful!

Linear regression

Decision trees

A model workflow

Workflows bind preprocessors and models

Explain that PCA that is a preprocessor / dimensionality reduction, used to decorrelate data

What is wrong with this?

Why a workflow()?

• Workflows handle new data better than base R tools in terms of new factor levels

• You can use other preprocessors besides formulas (more on feature engineering tomorrow!)

• They can help organize your work when working with multiple models

• Most importantly, a workflow captures the entire modeling process: fit() and predict() apply to the preprocessing steps in addition to the actual model fit

Two ways workflows handle levels better than base R:

• Enforces that new levels are not allowed at prediction time (this is an optional check that can be turned off)

• Restores missing levels that were present at fit time, but happen to be missing at prediction time (like, if your “new” data just doesn’t have an instance of that level)

A model workflow

tree_spec <-
  decision_tree() %>% 
  set_mode("regression")

tree_spec %>% 
  fit(latency ~ ., data = frog_train) 
#> parsnip model object
#> 
#> n= 456 
#> 
#> node), split, n, deviance, yval
#>       * denotes terminal node
#> 
#>  1) root 456 2197966.00  92.90351  
#>    2) age>=4.947975 256  252347.40  60.89844  
#>      4) treatment=control 131   91424.06  48.42748 *
#>      5) treatment=gentamicin 125  119197.90  73.96800 *
#>    3) age< 4.947975 200 1347741.00 133.87000  
#>      6) treatment=control 140  986790.70 118.25710  
#>       12) reflex=mid,full 129  754363.70 111.56590 *
#>       13) reflex=low 11  158918.20 196.72730 *
#>      7) treatment=gentamicin 60  247194.60 170.30000  
#>       14) age< 4.664439 30  102190.20 147.83330  
#>         28) age>=4.566638 22   53953.86 129.77270 *
#>         29) age< 4.566638 8   21326.00 197.50000 *
#>       15) age>=4.664439 30  114719.40 192.76670 *

A model workflow

tree_spec <-
  decision_tree() %>% 
  set_mode("regression")

workflow() %>%
  add_formula(latency ~ .) %>%
  add_model(tree_spec) %>%
  fit(data = frog_train) 
#> ══ Workflow [trained] ════════════════════════════════════════════════
#> Preprocessor: Formula
#> Model: decision_tree()
#> 
#> ── Preprocessor ──────────────────────────────────────────────────────
#> latency ~ .
#> 
#> ── Model ─────────────────────────────────────────────────────────────
#> n= 456 
#> 
#> node), split, n, deviance, yval
#>       * denotes terminal node
#> 
#>  1) root 456 2197966.00  92.90351  
#>    2) age>=4.947975 256  252347.40  60.89844  
#>      4) treatment=control 131   91424.06  48.42748 *
#>      5) treatment=gentamicin 125  119197.90  73.96800 *
#>    3) age< 4.947975 200 1347741.00 133.87000  
#>      6) treatment=control 140  986790.70 118.25710  
#>       12) reflex=mid,full 129  754363.70 111.56590 *
#>       13) reflex=low 11  158918.20 196.72730 *
#>      7) treatment=gentamicin 60  247194.60 170.30000  
#>       14) age< 4.664439 30  102190.20 147.83330  
#>         28) age>=4.566638 22   53953.86 129.77270 *
#>         29) age< 4.566638 8   21326.00 197.50000 *
#>       15) age>=4.664439 30  114719.40 192.76670 *

A model workflow

tree_spec <-
  decision_tree() %>% 
  set_mode("regression")

workflow(latency ~ ., tree_spec) %>% 
  fit(data = frog_train) 
#> ══ Workflow [trained] ════════════════════════════════════════════════
#> Preprocessor: Formula
#> Model: decision_tree()
#> 
#> ── Preprocessor ──────────────────────────────────────────────────────
#> latency ~ .
#> 
#> ── Model ─────────────────────────────────────────────────────────────
#> n= 456 
#> 
#> node), split, n, deviance, yval
#>       * denotes terminal node
#> 
#>  1) root 456 2197966.00  92.90351  
#>    2) age>=4.947975 256  252347.40  60.89844  
#>      4) treatment=control 131   91424.06  48.42748 *
#>      5) treatment=gentamicin 125  119197.90  73.96800 *
#>    3) age< 4.947975 200 1347741.00 133.87000  
#>      6) treatment=control 140  986790.70 118.25710  
#>       12) reflex=mid,full 129  754363.70 111.56590 *
#>       13) reflex=low 11  158918.20 196.72730 *
#>      7) treatment=gentamicin 60  247194.60 170.30000  
#>       14) age< 4.664439 30  102190.20 147.83330  
#>         28) age>=4.566638 22   53953.86 129.77270 *
#>         29) age< 4.566638 8   21326.00 197.50000 *
#>       15) age>=4.664439 30  114719.40 192.76670 *

Your turn

Run the tree_wflow chunk in your .qmd.

Edit this code so it uses a linear model.

05:00

Predict with your model

How do you use your new tree_fit model?

tree_spec <-
  decision_tree() %>% 
  set_mode("regression")

tree_fit <-
  workflow(latency ~ ., tree_spec) %>% 
  fit(data = frog_train) 

Your turn

Run:

predict(tree_fit, new_data = frog_test)

What do you get?

03:00

Your turn

Run:

augment(tree_fit, new_data = frog_test)

What do you get?

03:00

The tidymodels prediction guarantee!

• The predictions will always be inside a tibble
• The column names and types are unsurprising and predictable
• The number of rows in new_data and the output are the same

Understand your model

How do you understand your new tree_fit model?

Understand your model

How do you understand your new tree_fit model?

library(rpart.plot)
tree_fit %>%
  extract_fit_engine() %>%
  rpart.plot(roundint = FALSE)

You can extract_*() several components of your fitted workflow.

roundint = FALSE is only to quiet a warning

Understand your model

How do you understand your new tree_fit model?

You can use your fitted workflow for model and/or prediction explanations:

• overall variable importance, such as with the vip package

• flexible model explainers, such as with the DALEXtra package

Learn more at https://www.tmwr.org/explain.html

Your turn

Extract the model engine object from your fitted linear workflow.

⚠️ Never predict() with any extracted components!

05:00

Afterward, ask what kind of object people got from the extraction, and what they did with it (e.g. give it to summary(), plot(), broom::tidy() ). Live code along

Deploy your model

Deploying a model

How do you use your new tree_fit model in production?

library(vetiver)
v <- vetiver_model(tree_fit, "frog_hatching")
v
#> 
#> ── frog_hatching ─ <bundled_workflow> model for deployment 
#> A rpart regression modeling workflow using 4 features

Learn more at https://vetiver.rstudio.com

Deploy your model

How do you use your new model tree_fit in production?

library(plumber)
pr() %>%
  vetiver_api(v)
#> # Plumber router with 3 endpoints, 4 filters, and 1 sub-router.
#> # Use `pr_run()` on this object to start the API.
#> ├──[queryString]
#> ├──[body]
#> ├──[cookieParser]
#> ├──[sharedSecret]
#> ├──/logo
#> │  │ # Plumber static router serving from directory: /Users/emilhvitfeldt/Library/R/arm64/4.2/library/vetiver
#> ├──/metadata (GET)
#> ├──/ping (GET)
#> └──/predict (POST)

Learn more at https://vetiver.rstudio.com

Live-code making a prediction

Your turn

Run the vetiver chunk in your .qmd.

Check out the automated visual documentation.

05:00