Fit Local Linear Model and Compute Feature Importance

Fit Local Linear Model and Compute Feature Importance

Usage

fit_local_model(
  perturbations,
  predictor_vars,
  nfolds = 50,
  alpha = 1,
  class_names = c("A", "B")
)

Arguments

perturbations

Data frame of perturbations with predictions

predictor_vars

Character vector of predictor variable names

nfolds

Number of folds for cross-validation (default: 50)

alpha

Elastic net mixing parameter (default: 1 for lasso)

class_names

Character vector of class names for binary classification

Value

A list containing glm_predictions, importances, and the fitted model

Examples

 perturbations <- data.frame(
   x1 = c(1, 2, 3),
   x2 = c(4, 5, 6),
   pred = c("A", "A", "A")
 )

 result <- fit_local_model(
   perturbations,
   predictor_vars = c("x1", "x2")
 )