xspliner - Using surrogate black-boxes to train interpretable spline based additive models

xspliner’s pipeline: model %>% xspline(…) and analyze

Installation from CRAN

install.packages("xspliner")

Installation from Github

devtools::install_github("ModelOriented/xspliner")

News

Reference Manual

DEMO

library(xspliner)
library(randomForest)
library(pdp)
data(boston)
set.seed(123)
# fitting random forest model
model_rf <- randomForest(cmedv ~ lstat + ptratio + age, data = boston)

# building GLM (with standard black box response - Partial Dependence)
xspliner <- xspline(model_rf)

# see standard glm results
summary(xspliner)

# see ptratio variable transformation
plot(xspliner, "ptratio")

# compare xspliner and base model responses
plot(xspliner, model = model_rf, data = boston)

For more info check project vignettes or examples.

Further work

See github issues