Global Surrogate Tree

Source: R/light_global_surrogate.R

Model predictions are modelled by a single decision tree, serving as an easy to interprete surrogate to the original model. As suggested in Molnar (see reference below), the quality of the surrogate tree can be measured by its R-squared. The size of the tree can be modified by passing ... arguments to rpart::rpart().

light_global_surrogate(x, ...)

# Default S3 method
light_global_surrogate(x, ...)

# S3 method for class 'flashlight'
light_global_surrogate(
  x,
  data = x$data,
  by = x$by,
  v = NULL,
  use_linkinv = TRUE,
  n_max = Inf,
  seed = NULL,
  keep_max_levels = 4L,
  ...
)

# S3 method for class 'multiflashlight'
light_global_surrogate(x, ...)

Arguments

x

An object of class "flashlight" or "multiflashlight".

...

Arguments passed to rpart::rpart(), such as maxdepth.

data

An optional data.frame.

by

An optional vector of column names used to additionally group the results. For each group, a separate tree is grown.

v

Vector of variables used in the surrogate model. Defaults to all variables in data except "by", "w" and "y".

use_linkinv

Should retransformation function be applied? Default is TRUE.

n_max

Maximum number of data rows to consider to build the tree.

seed

An integer random seed used to select data rows if n_max is lower than the number of data rows.

keep_max_levels

Number of levels of categorical and factor variables to keep. Other levels are combined to a level "Other". This prevents rpart::rpart() to take too long to split non-numeric variables with many levels.

Value

An object of class "light_global_surrogate" with the following elements:

  • data A tibble with results.

  • by Same as input by.

Methods (by class)

  • light_global_surrogate(default): Default method not implemented yet.

  • light_global_surrogate(flashlight): Surrogate model for a flashlight.

  • light_global_surrogate(multiflashlight): Surrogate model for a multiflashlight.

References

Molnar C. (2019). Interpretable Machine Learning.

Examples

fit <- lm(Sepal.Length ~ ., data = iris)
x <- flashlight(model = fit, label = "lm", data = iris)
sur <- light_global_surrogate(x)
sur$data$r_squared
#> [1] 0.9225605
plot(sur)