bl_imp.GeDSboost {GeDS}

Base Learner Importance for GeDSboost objects

Description

This function calculates the in-bag mean squared error (MSE) reduction ascribable to each of the base-learners with regards to the final prediction of the component-wise gradient boosted model encapsulated in a GeDSboost-Class object. Essentially, it measures the decrease in MSE attributable to each base-learner for every time it is selected across the boosting iterations, and aggregates them. This provides a measure on how much each base-learner contributes to the overall improvement in the model's accuracy, as reflected by the decrease in MSE. This function is adapted from varimp and is compatible with the available mboost-package methods for varimp, including plot, print and as.data.frame.

Usage

## S3 method for class 'GeDSboost'
bl_imp(object, boosting_iter_only = FALSE, ...)

Arguments

object

an object of class GeDSboost-Class.

boosting_iter_only

logical value, if TRUE then base-learner in-bag risk reduction is only computed across boosting iterations, i.e., without taking into account the initial learner.

...

potentially further arguments.

Details

See varimp for details.

Value

An object of class varimp with available plot, print and as.data.frame methods.

References

Hothorn T., Buehlmann P., Kneib T., Schmid M. and Hofner B. (2022). mboost: Model-Based Boosting. R package version 2.9-7, https://CRAN.R-project.org/package=mboost.

Examples

library(GeDS)
library(TH.data)
set.seed(290875)
data("bodyfat", package = "TH.data")
data = bodyfat
Gmodboost <- NGeDSboost(formula = DEXfat ~ f(hipcirc) + f(kneebreadth) + f(anthro3a),
                        data = data, initial_learner = FALSE)
MSE_Gmodboost_linear <- mean((data$DEXfat - Gmodboost$predictions$pred_linear)^2)
MSE_Gmodboost_quadratic <- mean((data$DEXfat - Gmodboost$predictions$pred_quadratic)^2)
MSE_Gmodboost_cubic <- mean((data$DEXfat - Gmodboost$predictions$pred_cubic)^2)

# Print MSE
cat("\n", "MEAN SQUARED ERROR", "\n",
    "Linear NGeDSboost:", MSE_Gmodboost_linear, "\n",
    "Quadratic NGeDSboost:", MSE_Gmodboost_quadratic, "\n",
    "Cubic NGeDSboost:", MSE_Gmodboost_cubic, "\n")

# Base Learner Importance
bl_imp <- bl_imp(Gmodboost)
print(bl_imp)
plot(bl_imp)