xgrove {xgrove}

Explanation groves

Description

Compute surrogate groves to explain predictive machine learning model and analyze complexity vs. explanatory power.

Usage

xgrove(
  model,
  data,
  ntrees = c(4, 8, 16, 32, 64, 128),
  pfun = NULL,
  shrink = 1,
  b.frac = 1,
  seed = 42,
  ...
)

Arguments

model

A model with corresponding predict function that returns numeric values.

data

Data that must not (!) contain the target variable.

ntrees

Sequence of integers: number of boosting trees for rule extraction.

pfun

Optional predict function function(model, data) returning a real number. Default is the predict() method of the model.

shrink

Sets the shrinkage argument for the internal call of gbm. As the model usually has a deterministic response the default is 1 different to the default of gbm applied train a model based on data.

b.frac

Sets the bag.fraction argument for the internal call of gbm. As the model usually has a deterministic response the default is 1 different to the default of gbm applied train a model based on data.

seed

Seed for the random number generator to ensure reproducible results (e.g. for the default bag.fraction < 1 in boosting).

...

Further arguments to be passed to gbm or the predict() method of the model.

Details

A surrogate grove is trained via gradient boosting using gbm on data with the predictions of using of the model as target variable. Note that data must not contain the original target variable! The boosting model is trained using stumps of depth 1. The resulting interpretation is extracted from pretty.gbm.tree.

Value

List of the results:

explanation

Matrix containing tree sizes, rules, explainability {\Upsilon} and the correlation between the predictions of the explanation and the true model.

rules

Summary of the explanation grove: Rules with identical splits are aggegated. For numeric variables any splits are merge if they lead to identical parititions of the training data

groves

Rules of the explanation grove.

model

gbm model.

Author(s)

[e-mail]

References

• Szepannek, G. and von Holt, B.H. (2023): Can’t see the forest for the trees – analyzing groves to explain random forests, Behaviormetrika, DOI: 10.1007/s41237-023-00205-2.

• Szepannek, G. and Luebke, K.(2023): How much do we see? On the explainability of partial dependence plots for credit risk scoring, Argumenta Oeconomica 50, DOI: 10.15611/aoe.2023.1.07.

Examples

library(randomForest)
library(pdp)
data(boston)
set.seed(42)
rf <- randomForest(cmedv ~ ., data = boston)
data <- boston[,-3] # remove target variable
ntrees <- c(4,8,16,32,64,128)
xg <- xgrove(rf, data, ntrees)
xg
plot(xg)