Tidy your mental models

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# Tidy your mental models

### Alison Hill, Ph.D.

### AI Strategy & Innovation @IBM

### 2022-01-25
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## .big-text[Hello.]

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### Alison Hill

### Senior Data Scientist

### AI Strategy & Innovation @IBM

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## Teaching tidymodels

+ [tidymodels.org](https://www.tidymodels.org/)

+ Società Italiana di Statistica Medica ed Epidemiologia Clinica Virtual Conference (2021)

+ R / Medicine Virtual Conference (2020)

+ Becton, Dickinson and Company, Online (2020)

+ Children’s Hospital of Philadelphia, Online (2020)

+ rstudio::conf 2020, San Francisco, CA, January 27 & 28 Training Days (2020)

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## Timeline

Step 1: Learn tidymodels (~2 months)

Step 2: Design a 2-day workshop (~2 months)

Step 3: Teach it! (December 2019)

Step 4: Teach it again! (January 2020)

Step 5: Develop tidymodels.org (April 2020)

Step 6: Teaching remotely (https://www.apreshill.com/blog/2020-06-how-i-taught-tidymodels-virtually/)

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## What is hard about learning (and teaching) tidymodels?

+ Lots of .emphasis[packages] (to `::` or not to `::`?)

+ Lots of .emphasis[models] (stats background?)

+ Lots of .emphasis[concepts] (some = code; others = invisible)

+ Lots of .emphasis[methods] (randomness + resampling)

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## v1

https://www.apreshill.com/blog/2020-02-take-a-sad-script-make-it-better-tidymodels-edition/

## 🙈🙉🙊

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## What worked (for me)

+ My learner personas (+ their ?)

+ Sequencing (roughly)

+ Pacing (roughly)

+ Scoping

+ Bite-sized, timed exercises ("No code chunk left blank!")

+ Switching back-and-forth between datasets (surprise!)

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## Lessons learned

+ Give folks a "whole game" split-to-fit-to-tune case study at the end

+ Workflows is a dry package; use recipes to spice it up earlier

+ Cross-validation and tuning are like PB+J, so teach it like that

+ If time constrained, transition quickly from decision trees to random forests

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## Visualizing models

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```r
*# pick model
*lm_mod <-
* parsnip::linear_reg(engine = "lm") %>%
* parsnip::set_mode("regression")

lm_mod %>% 
  fit(body_mass_g ~ flipper_length_mm, 
      data = penguins) 
# parsnip model object
# 
# Fit time:  5ms 
# 
# Call:
# stats::lm(formula = body_mass_g ~ flipper_length_mm, data = data)
# 
# Coefficients:
#       (Intercept)  flipper_length_mm  
#          -5872.09              50.15
```

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<img src="index_files/figure-html/penguins-lm-1.png" width="504" style="display: block; margin: auto;" />

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```r
*# pick model
*rt_mod <-
* parsnip::decision_tree(engine = "rpart") %>%
* parsnip::set_mode("regression")

rt_mod %>% 
  fit(body_mass_g ~ flipper_length_mm, 
      data = penguins)
# parsnip model object
# 
# Fit time:  5ms 
# n= 333 
# 
# node), split, n, deviance, yval
#       * denotes terminal node
# 
#  1) root 333 215259700 4207.057  
#    2) flipper_length_mm< 206.5 208  38996800 3702.524  
#      4) flipper_length_mm< 193.5 129  19162970 3555.039 *
#      5) flipper_length_mm>=193.5 79  12445890 3943.354 *
#    3) flipper_length_mm>=206.5 125  35211680 5046.600  
#      6) flipper_length_mm< 214.5 49   8152398 4614.796 *
#      7) flipper_length_mm>=214.5 76  12032500 5325.000  
#       14) flipper_length_mm< 220.5 41   5359848 5134.756 *
#       15) flipper_length_mm>=220.5 35   3450464 5547.857 *
```

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<img src="index_files/figure-html/penguins-rt-1.png" width="504" style="display: block; margin: auto;" />

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# Thank you!

### Find me at...

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