Random Forest Regression

Runs a random forest regression.

rfRegress(
  gridNumber = 10,
  recipe = rec,
  folds = cvFolds,
  train = train_df,
  test = test_df,
  response = response,
  treeNum = 100,
  calcFeatImp = TRUE,
  evalMetric = "rmse"
)

Arguments

gridNumber

Numeric. Size of the grid you want XGBoost to explore. Default is 10.

recipe

A recipe object.

folds

A rsample::vfolds_cv object.

train

Data frame/tibble. The training data set.

test

Data frame/tibble. The testing data set.

response

Character. The variable that is the response for analysis.

treeNum

Numeric. The number of trees to evaluate your model with. Default is 100.

calcFeatImp

Logical. Do you want to calculate feature importance for your model? If not, set = FALSE.

evalMetric

Character. The regression metric you want to evaluate the model's accuracy on. Default is RMSE. Can choose from the following:

  • rmse

  • mae

  • rsq

  • mase

  • ccc

  • icc

  • huber_loss

Value

A list with the following features:

  • Training set predictions

  • Training set evaluation on RMSE and MAE

  • Testing set predictions

  • Testing set evaluation on RMSE and MAE

  • Feature importance plot

  • Feature importance table (with exact values)

  • Tuned model object

Details

What the model tunes:

  • mtry: The number of predictors that will be randomly sampled at each split when creating the tree models.

  • min_n: The minimum number of data points in a node that are required for the node to be split further.

What you set specifically:

  • trees: Default is 100. Sets the number of trees contained in the ensemble. A larger values increases runtime but (ideally) leads to more robust outcomes.

Examples

#' library(easytidymodels)
library(dplyr)
library(recipes)
utils::data(penguins, package = "modeldata")

#Define your response variable and formula object here
resp <- "bill_length_mm"
formula <- stats::as.formula(paste(resp, ".", sep="~"))

#Split data into training and testing sets
split <- trainTestSplit(penguins, responseVar = resp)

#Create recipe for feature engineering for dataset, varies based on data working with
rec <- recipe(formula, split$train) %>% prep()
train_df <- bake(rec, split$train)
test_df <- bake(rec, split$test)
folds <- cvFolds(train_df)
#rfReg <- rfRegress(recipe = rec, response = resp, folds = folds,
#train = train_df, test = test_df, calcFeatImp = TRUE)

#Visualize training data and its predictions
#rfReg$trainPred %>% select(.pred, !!resp)

#View how model metrics for RMSE, R-Squared, and MAE look for training data
#rfReg$trainScore

#Visualize testing data and its predictions
#rfReg$testPred %>% select(.pred, !!resp)

#View how model metrics for RMSE, R-Squared, and MAE look for testing data
#rfReg$testScore

#See the final model chosen by RF based on optimizing for your chosen evaluation metric
#rfReg$final

#See how model fit looks based on another evaluation metric
#rfReg$tune %>% tune::select_best("rmse")

#See feature importance of model
#rfReg$featImpPlot