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tidymodels

Dr. D’Agostino McGowan

1 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

tidymodels

tidymodels.org

  • tidymodels is an opinionated collection of R packages designed for modeling and statistical analysis.
  • All packages share an underlying philosophy and a common grammar.
2 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Step 1: Specify the model

  • Pick the model
3 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Step 1: Specify the model

  • Pick the model
  • Set the engine
3 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Specify the model

linear_reg() %>%
  set_engine("lm")
4 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Specify the model

linear_reg() %>%
  set_engine("glmnet")
5 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Specify the model

linear_reg() %>%
  set_engine("spark")
6 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Specify the model

decision_tree() %>%
  set_engine("ranger")
7 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Specify the model

  • All available models:

https://www.tidymodels.org

8 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Specify Model

Write a pipe that creates a model that uses lm() to fit a linear regression using tidymodels. Save it as lm_spec and look at the object. What does it return?

Hint: you'll need https://www.tidymodels.org

02:00
9 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse 
lm_spec <- 
  linear_reg() %>% # Pick linear regression
  set_engine(engine = "lm") # set engine
lm_spec
## Linear Regression Model Specification (regression)
## 
## Computational engine: lm
10 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Fit the data

  • You can train your model using the fit() function
fit(lm_spec,
    mpg ~ horsepower,
    data = Auto)
## parsnip model object
## 
## Fit time:  7ms 
## 
## Call:
## stats::lm(formula = mpg ~ horsepower, data = data)
## 
## Coefficients:
## (Intercept)   horsepower  
##     39.9359      -0.1578
11 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Fit Model

Fit the model:

library(ISLR)
lm_fit <- fit(lm_spec,
              mpg ~ horsepower,
              data = Auto)
lm_fit

Does this give the same results as

lm(mpg ~ horsepower, data = Auto)
01:30
12 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Get predictions

lm_fit %>%
  predict(new_data = Auto)
13 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Get predictions

lm_fit %>%
  predict(new_data = Auto)
  • Uses the predict() function
13 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Get predictions

lm_fit %>%
  predict(new_data = Auto)
  • Uses the predict() function
  • ‼️ new_data has an underscore
13 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Get predictions

lm_fit %>%
  predict(new_data = Auto)
  • Uses the predict() function
  • ‼️ new_data has an underscore
  • 😄 This automagically creates a data frame
13 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Get predictions

lm_fit %>%
  predict(new_data = Auto) %>%
  bind_cols(Auto)
## # A tibble: 392 x 10
##    .pred   mpg cylinders displacement horsepower weight acceleration  year
##  * <dbl> <dbl>     <dbl>        <dbl>      <dbl>  <dbl>        <dbl> <dbl>
##  1 19.4     18         8          307        130   3504         12      70
##  2 13.9     15         8          350        165   3693         11.5    70
##  3 16.3     18         8          318        150   3436         11      70
##  4 16.3     16         8          304        150   3433         12      70
##  5 17.8     17         8          302        140   3449         10.5    70
##  6  8.68    15         8          429        198   4341         10      70
##  7  5.21    14         8          454        220   4354          9      70
##  8  6.00    14         8          440        215   4312          8.5    70
##  9  4.42    14         8          455        225   4425         10      70
## 10  9.95    15         8          390        190   3850          8.5    70
## # … with 382 more rows, and 2 more variables: origin <dbl>, name <fct>
14 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse
01:30

Get predictions

Edit the code below to add the original data to the predicted data.

mpg_pred <- lm_fit %>% 
  predict(new_data = Auto) %>% 
  ---
15 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Get predictions

mpg_pred <- lm_fit %>%
  predict(new_data = Auto) %>%
  bind_cols(Auto)
mpg_pred
## # A tibble: 392 x 10
##    .pred   mpg cylinders displacement horsepower weight acceleration  year
##  * <dbl> <dbl>     <dbl>        <dbl>      <dbl>  <dbl>        <dbl> <dbl>
##  1 19.4     18         8          307        130   3504         12      70
##  2 13.9     15         8          350        165   3693         11.5    70
##  3 16.3     18         8          318        150   3436         11      70
##  4 16.3     16         8          304        150   3433         12      70
##  5 17.8     17         8          302        140   3449         10.5    70
##  6  8.68    15         8          429        198   4341         10      70
##  7  5.21    14         8          454        220   4354          9      70
##  8  6.00    14         8          440        215   4312          8.5    70
##  9  4.42    14         8          455        225   4425         10      70
## 10  9.95    15         8          390        190   3850          8.5    70
## # … with 382 more rows, and 2 more variables: origin <dbl>, name <fct>
16 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Calculate the error

  • Root mean square error
mpg_pred %>%
  rmse(truth = mpg, estimate = .pred)
## # A tibble: 1 x 3
##   .metric .estimator .estimate
##   <chr>   <chr>          <dbl>
## 1 rmse    standard        4.89
17 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Calculate the error

  • Root mean square error
mpg_pred %>%
  rmse(truth = mpg, estimate = .pred)
## # A tibble: 1 x 3
##   .metric .estimator .estimate
##   <chr>   <chr>          <dbl>
## 1 rmse    standard        4.89

What is this estimate? (training error? testing error?)

17 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Validation set approach

Auto_split <- initial_split(Auto, prop = 0.5)
Auto_split
## <Analysis/Assess/Total>
## <196/196/392>
18 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Validation set approach

Auto_split <- initial_split(Auto, prop = 0.5)
Auto_split
## <Analysis/Assess/Total>
## <196/196/392>
  • Extract the training and testing data
training(Auto_split)
testing(Auto_split)
18 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

Validation set approach

Auto_train <- training(Auto_split)
Auto_train
## # A tibble: 196 x 9
##      mpg cylinders displacement horsepower weight acceleration  year origin
##    <dbl>     <dbl>        <dbl>      <dbl>  <dbl>        <dbl> <dbl>  <dbl>
##  1    14         8          454        220   4354          9      70      1
##  2    15         8          383        170   3563         10      70      1
##  3    14         8          340        160   3609          8      70      1
##  4    14         8          455        225   3086         10      70      1
##  5    24         4          113         95   2372         15      70      3
##  6    18         6          199         97   2774         15.5    70      1
##  7    21         6          200         85   2587         16      70      1
##  8    25         4          110         87   2672         17.5    70      2
##  9    24         4          107         90   2430         14.5    70      2
## 10    25         4          104         95   2375         17.5    70      2
## # … with 186 more rows, and 1 more variable: name <fct>
19 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse
04:00

Validation Set

Copy the code below, fill in the blanks to fit a model on the training data then calculate the test RMSE.

set.seed(100)
Auto_split  <- ________
Auto_train  <- ________
Auto_test   <- ________
lm_fit      <- fit(lm_spec, 
                   mpg ~ horsepower, 
                   data = ________)
mpg_pred  <- ________ %>% 
  predict(new_data = ________) %>% 
  bind_cols(________)
rmse(________, truth = ________, estimate = ________)
20 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

A faster way!

  • You can use last_fit() and specify the split
  • This will automatically train the data on the train data from the split
  • Instead of specifying which metric to calculate (with rmse as before) you can just use collect_metrics() and it will automatically calculate the metrics on the test data from the split
set.seed(100)
Auto_split <- initial_split(Auto, prop = 0.5)
lm_fit <- last_fit(lm_spec,
                   mpg ~ horsepower,
                   split = Auto_split)
lm_fit %>%
  collect_metrics()
## # A tibble: 2 x 3
##   .metric .estimator .estimate
##   <chr>   <chr>          <dbl>
## 1 rmse    standard       4.87 
## 2 rsq     standard       0.625
21 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

What about cross validation?

Auto_cv <- vfold_cv(Auto, v = 5)
Auto_cv
## #  5-fold cross-validation 
## # A tibble: 5 x 2
##   splits           id   
##   <list>           <chr>
## 1 <split [313/79]> Fold1
## 2 <split [313/79]> Fold2
## 3 <split [314/78]> Fold3
## 4 <split [314/78]> Fold4
## 5 <split [314/78]> Fold5
22 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

What about cross validation?

23 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

What about cross validation?

fit_resamples(lm_spec,
              mpg ~ horsepower,
              resamples = Auto_cv)
23 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

What about cross validation?

fit_resamples(lm_spec,
              mpg ~ horsepower,
              resamples = Auto_cv)
## #  5-fold cross-validation 
## # A tibble: 5 x 4
##   splits           id    .metrics         .notes          
##   <list>           <chr> <list>           <list>          
## 1 <split [313/79]> Fold1 <tibble [2 × 3]> <tibble [0 × 1]>
## 2 <split [313/79]> Fold2 <tibble [2 × 3]> <tibble [0 × 1]>
## 3 <split [314/78]> Fold3 <tibble [2 × 3]> <tibble [0 × 1]>
## 4 <split [314/78]> Fold4 <tibble [2 × 3]> <tibble [0 × 1]>
## 5 <split [314/78]> Fold5 <tibble [2 × 3]> <tibble [0 × 1]>
24 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

What about cross validation?

How do we get the metrics out? With collect_metrics() again!

25 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

What about cross validation?

How do we get the metrics out? With collect_metrics() again!

results <- fit_resamples(lm_spec,
                         mpg ~ horsepower,
                         resamples = Auto_cv)
results %>%
  collect_metrics()
## # A tibble: 2 x 5
##   .metric .estimator  mean     n std_err
##   <chr>   <chr>      <dbl> <int>   <dbl>
## 1 rmse    standard   4.93      5  0.0779
## 2 rsq     standard   0.611     5  0.0277
25 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse
02:00

K-fold cross validation

Edit the code below to get the 5-fold cross validation error rate for the following model:

mpg=β0+β1horsepower+β2horsepower2+ϵ

Auto_cv <- vfold_cv(Auto, v = 5)
results <- fit_resamples(lm_spec,
                         ----,
                         resamples = ---)
results %>%
  collect_metrics()
  • What do you think rsq is?
26 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse
27 / 27
Dr. Lucy D'Agostino McGowan adapted from Alison Hill's Introduction to ML with the Tidyverse

tidymodels

tidymodels.org

  • tidymodels is an opinionated collection of R packages designed for modeling and statistical analysis.
  • All packages share an underlying philosophy and a common grammar.
2 / 27
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