Multi Adaptive Regressive Spline

Fits a MARS regression model.

marsRegress(
  response = response,
  recipe = rec,
  folds = folds,
  train = train_df,
  test = test_df,
  gridNumber = 10,
  evalMetric = "rmse"
)

Arguments

response

Character. The variable that is the response for analysis.

recipe

A recipes::recipe object.

folds

A rsample::vfolds_cv object.

train

Data frame/tibble. The training data set.

test

Data frame/tibble. The testing data set.

gridNumber

Numeric. The size of the grid to tune on. Default is 15.

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 elements:

  • Training set predictions

  • Training set evaluation on RMSE and MAE

  • Testing set predictions

  • Testing set evaluation on RMSE and MAE

  • Tuned model object

Details

Note - Tunes the following parameters:

  • num_terms: The number of features that will be retained in the final model.

  • prod_degree: The highest possible degree of interaction between features. A value of 1 indicates an additive model while a value of 2 allows, but does not guarantee, two-way interactions between features.

  • prune_method: The type of pruning. Possible values are listed in ?earth.

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)

#Fit a MARS regression object (commented out only due to long run time)
#marsReg <- marsRegress(recipe = rec, response = resp,
#folds = folds, train = train_df, test = test_df, evalMetric = "rmse")

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

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

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

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

#See the final model chosen for MARS based on optimizing for your chosen evaluation metric
#marsReg$final

#See how model fit looks based on another evaluation metric
#marsReg$tune %>% tune::show_best("mae")