Finding Subgroups with Conformal Trees

Usage

r2p(
  data,
  target,
  learner,
  cv_folds = 1,
  alpha = 0.1,
  gamma = 0.01,
  lambda = 0.5,
  max_groups = 5
)

Arguments

data: (data.frame)
data set for model training and uncertainty estimation.
target: (string)
name of the target variable. The target must be a numeric variable.
learner: (model_spec)
the learner for training the prediction model. See parsnip::model_spec() for details.
cv_folds: (count)
number of CV+ folds.
alpha: (proportion)
miscoverage rate.
gamma: (proportion)
regularization parameter ensuring that reduction in the impurity of the confident homogeneity is sufficiently large.
lambda: (proportion)
balance parameter, quantifying the impact of the average interval length relative to the average absolute deviation (i.e. interval width vs. average absolute deviation)
max_groups: (count)
maximum number of subgroups.

Value

The tree.

Examples

library(tidymodels)
library(ranger)
data(bikes)
set.seed(1234)
# Initialize learner:
forest <- rand_forest() %>%
  set_mode("regression") %>%
  set_engine("ranger")
# Detect subgroups:
groups <- r2p(
  data = bikes,
  target = "count",
  learner = forest,
  cv_folds = 10,
  alpha = 0.1,
  gamma = 0.01,
  lambda = 0.5,
  max_groups = 2
)
groups
#> Conformal tree with 2 subgroups:
#> [1] root
#> |   [2] weekday in Sun, Sat: *
#> |   [3] weekday in Mon, Tue, Wed, Thu, Fri: *
#> ---
#> * terminal nodes (subgroups)