Complete Local Interpretation Pipeline

Usage

kumquat(
  model_bundle,
  data,
  pois,
  perturbations = NULL,
  radius = 0.1,
  step = 0.01,
  predictor_vars = c("x", "y"),
  nfolds = 50,
  alpha = 1,
  class_names = c("A", "B"),
  predict_func = stats::predict
)

Arguments

model_bundle: A trained model held in a bundle::bundle()
data: Training data
pois: Points of interest (data rows)
perturbations: A list of data.frames of perturbations to be used to fit the local model
radius: Perturbation radius (default: 0.1)
step: Perturbation step size (default: 0.01)
predictor_vars: Character vector of predictor variable names
nfolds: Number of CV folds (default: 50)
alpha: Elastic net parameter (default: 1)
class_names: Character vector of class names
predict_func: A function that takes in two arguments: model and data and returns a vector of factors

Value

A list containing perturbations, predictions, and local model results

Examples

data(d_vertical)
rfmodel <- randomForest::randomForest(
 class ~ x + y,
 data = d_vertical
)
# Bundle model up
rfmodel_bundled <- bundle::bundle(rfmodel)
ks <- kumquat(
 rfmodel_bundled,
 d_vertical,
  d_vertical[1,],
  class_names = unique(d_vertical$class)
)
#> INFO [2026-06-19 02:26:51] Picking kumquats for row: 1