Hyperparameter tuning in caret

Hyperparameter Tuning in R

Dr. Shirin Elsinghorst

Senior Data Scientist

Voter dataset from US 2016 election

Split intro training and test set

library(tidyverse)
glimpse(voters_train_data)

Observations: 6,692
Variables: 42
$ turnout16_2016       <chr> "Did not vote", "Did not vote", "Did not vote", "Did not vote", ...
$ RIGGED_SYSTEM_1_2016 <int> 2, 2, 3, 2, 2, 3, 3, 1, 2, 3, 4, 4, 4, 3, 1, 2, 2, 2, 3, 2, 1, 2, 3, 2, 1, ...
$ RIGGED_SYSTEM_2_2016 <int> 3, 3, 2, 2, 3, 3, 2, 2, 1, 2, 4, 2, 3, 2, 3, 4, 3, 2, 2, 2, 4, 1, 2, 2, 3, ...
$ RIGGED_SYSTEM_3_2016 <int> 1, 1, 3, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 3, 1, 3, 2, 1, 1, ...
$ RIGGED_SYSTEM_4_2016 <int> 2, 1, 2, 2, 2, 2, 2, 2, 1, 3, 3, 1, 3, 3, 1, 3, 3, 2, 1, 1, 1, 2, 1, 2, 2, ...
$ RIGGED_SYSTEM_5_2016 <int> 1, 2, 2, 2, 2, 3, 1, 1, 2, 3, 2, 2, 1, 3, 1, 1, 2, 2, 1, 2, 1, 2, 2, 2, 1, ...
$ RIGGED_SYSTEM_6_2016 <int> 1, 1, 2, 1, 2, 2, 2, 1, 2, 2, 1, 3, 1, 3, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, ...
$ track_2016           <int> 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 1, 2, 2, 3, 2, 2, 2, 2, 3, 2, 2, ...
...

Let's train another model with caret

Stochastic Gradient Boosting

library(caret)
library(tictoc)
fitControl <- trainControl(method = "repeatedcv", number = 3, repeats = 5)
tic()
set.seed(42)
gbm_model_voters <- train(turnout16_2016 ~ ., 
                   data = voters_train_data, 
                   method = "gbm", 
                   trControl = fitControl,
                   verbose = FALSE)
toc()

32.934 sec elapsed

Let's train another model with caret

gbm_model_voters

Stochastic Gradient Boosting 
...
Resampling results across tuning parameters:
  interaction.depth  n.trees  Accuracy   Kappa        
  1                   50      0.9604603  -0.0001774346
 ...
Tuning parameter 'shrinkage' was held constant at a value of 0.1
Tuning parameter 'n.minobsinnode' was held constant at a value of 10

Accuracy was used to select the optimal model using the largest value.
The final values used for the model were n.trees = 50,
interaction.depth = 1, shrinkage = 0.1 and n.minobsinnode = 10.

Cartesian grid search with caret

Define a Cartesian grid of hyperparameters:

man_grid <-  expand.grid(n.trees = c(100, 200, 250), interaction.depth = c(1, 4, 6), 
                         shrinkage = 0.1, n.minobsinnode = 10)

fitControl <- trainControl(method = "repeatedcv", number = 3, repeats = 5)
tic()
set.seed(42)
gbm_model_voters_grid <- train(turnout16_2016 ~ ., 
                   data = voters_train_data, 
                   method = "gbm", 
                   trControl = fitControl,
                   verbose = FALSE,
                   tuneGrid = man_grid)
toc()

85.745 sec elapsed

Cartesian grid search with caret

gbm_model_voters_grid

Stochastic Gradient Boosting 
...
Resampling results across tuning parameters:
  interaction.depth  n.trees  Accuracy   Kappa      
  1                  100      0.9603108  0.000912769
 ...
Tuning parameter 'shrinkage' was held constant at a value of 0.1
Tuning parameter 'n.minobsinnode' was held constant at a value of 10

Accuracy was used to select the optimal model using the largest value.
The final values used for the model were n.trees = 100, 
interaction.depth = 1, shrinkage = 0.1 and n.minobsinnode = 10.

Plot hyperparameter models

plot(gbm_model_voters_grid)

plot(gbm_model_voters_grid, 
     metric = "Kappa", 
     plotType = "level")

Test it out for yourself!

Hyperparameter Tuning in R