examples/compare_with_gam.R

In ModelOriented/xspliner: Assisted Model Building, using Surrogate Black-Box Models to Train Interpretable Spline Based Additive Models

library(randomForest)
library(pdp)
library(xspliner)
data(boston)
set.seed(101)

# build random forest model:
boston.rf <- randomForest(cmedv ~ lstat + ptratio + age, data = boston)

# build xspliner model with specified response method and approximation options
model_pdp <- xspline(
  cmedv ~
    xs(lstat, transition = list(k = 6), effect = list(type = "pdp", grid.resolution = 60)) +
    xs(ptratio, transition = list(k = 4), effect = list(type = "pdp", grid.resolution = 40)) +
    age,
  model = boston.rf,
  data = boston
)

model_ale <- xspline(
  cmedv ~
    xs(lstat, transition = list(k = 6), effect = list(type = "ale", K = 60)) +
    xs(ptratio, transition = list(k = 4), effect = list(type = "ale", K = 40)) +
    age,
  model = boston.rf,
  data = boston
)

# check model summary
summary(model_pdp, model = boston.rf, newdata = boston)

summary(model_ale, model = boston.rf, newdata = boston)

# compare with standard gam model
summary(mgcv::gam(cmedv ~ s(lstat, k = 6) + s(ptratio, k = 4) + age, data = boston))