Plot the path of a forward feature selection

select_features_forward() scores every candidate at every step and keeps the best; its history holds all of them. This draws the accepted variable's score at each step as the path, every other candidate's score at that step as a faint point, and the step at which the selection stopped in red. When it stopped because no candidate cleared tol, the path runs one step further to the best of the rejected candidates, drawn hollow and labelled "not added", so the stop reads as a flattening rather than a cut. The picture then says whether the last variable was a clear gain or the first that happened to clear tol, and whether the runner-up would have done as well. The scores are the selection's own cross-validated criterion, optimistically biased by the selection (see the help page's section on that); when a hold-out score was computed (select_on = "split") it is drawn as a separate mark at the final step and named in the caption.

Usage

# S3 method for class 'feature_selection'
plot(x, ...)

Arguments

x

The list returned by select_features_forward().

...

Ignored.

Value

A ggplot object.

See also

Other plotting: plot.aoa(), plot.block_size_sweep(), plot.gwr_model_selection(), plot.resolution_profile(), plot.sac_range(), plot.spatial_fit(), plot_calibration(), plot_cv_metrics(), plot_folds()

Examples

if (requireNamespace("ranger", quietly = TRUE) &&
    requireNamespace("ggplot2", quietly = TRUE)) {
  library(sf)
  set.seed(4)
  n <- 150
  dat <- st_as_sf(
    data.frame(x = 5e5 + runif(n, 0, 1000), y = 5e6 + runif(n, 0, 1000),
               a = rnorm(n), b = rnorm(n), c = rnorm(n)),
    coords = c("x", "y"), crs = 32632)
  dat$z <- 2 * dat$a - dat$b + rnorm(n, 0, 0.5)
  fit_fn <- function(train_sf, vars)
    fit_rf_model(train_sf, "z", vars, num_trees = 80, seed = 1)
  sel <- select_features_forward(dat, "z", c("a", "b", "c"), fit_fn = fit_fn,
                                 k = 3, quiet = TRUE)
  plot(sel)
}