View source: R/bart_functions.R
variable_importance | R Documentation |
This function computes the variable importance scores for a fitted BART (Bayesian Additive Regression Trees) model using a permutation-based approach. It measures the impact of each predictor variable on the model's performance by permuting the values of that variable and evaluating the change in performance (F-score is the performance metric).
variable_importance(bart_model, cutoff = 0, n_repeats = 10, seed = NULL)
bart_model |
A BART model object. |
cutoff |
A numeric threshold for converting predicted probabilities into presence-absence. |
n_repeats |
An integer indicating the number of times to repeat the permutation for each variable. |
seed |
An optional seed for random number generation. |
A data frame where each column corresponds to a predictor variable, and each row contains the variable importance scores across permutations.
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