Calculate Variable Importance for a figsr_fit Model

figsr_importance() computes the total reduction in residual impurity (Sum of Squares gain) attributable to each predictor feature across all trees in the fitted FIGS model.

Usage

figsr_importance(object, relative = TRUE)

Arguments

object

A fitted figsr_fit model object.

relative

Logical. If TRUE (default), the importance column is rescaled to percentages of the total gain; otherwise it repeats the raw gain.

Value

A tibble with one row per predictor and columns feature, gain (raw sum-of-squares reduction) and importance, sorted by decreasing importance.

Details

Each split node stores the reduction in residual sum of squares it achieved at the moment it was chosen. Importance for a feature is the sum of those gains over every split made on that feature, across all trees in the sum. Features never selected receive a gain of zero.

When the model was fitted with na_method = "mia", a split on whether a predictor is missing is reported on its own row, named missing(<predictor>), because such a split never consults the predictor's value. A split that merely sends missing values to one side is credited to the predictor.

Examples

set.seed(42)
df <- data.frame(x1 = rnorm(50), x2 = rnorm(50))
df$y <- 2 * (df$x1 > 0) + rnorm(50, sd = 0.2)
fit <- figs(y ~ x1 + x2, data = df, max_splits = 3)
figsr_importance(fit)
#> # A tibble: 2 × 3
#>   feature  gain importance
#>   <chr>   <dbl>      <dbl>
#> 1 x1       52.2        100
#> 2 x2        0            0