Prints the result, the call that produced it, and a ranked score table.
Usage
# S3 method for class 'fs_result'
summary(object, n = 20L, ...)Details
Everything print() shows, then the recorded call, then – for methods
that produce per-feature scores – a table of every scored feature, ranked
strongest first, with columns feature, score (four
significant digits) and selected (* marks the features that were kept).
The table is truncated to n rows with a "... (m more)" tail.
The ranking direction follows the method. For most methods a larger score
means a stronger feature, and rows are ordered by decreasing score, so a
negative importance (a feature that did worse than its permuted copy)
ranks below every positive one. The exception is fs_lasso,
whose scores are signed standardized coefficients: those are ordered by
decreasing absolute value, since the sign is the direction of the effect,
not its strength. For
methods that score by p-value, where smaller is better, rows are ordered
ascending instead, so the most significant feature is listed first. The
same ascending order is used for the threshold filters
(fs_unsupervised, fs_supervised) when they kept
the low side of the threshold (direction = "below" with
action = "keep", or "above" with "remove"), so the
kept features come first.
Examples
res <- fs_unsupervised(
data.frame(a = c(1, 5, 2, 8), b = c(1, 1, 1, 1)),
method = "variance", threshold = 0.5
)
summary(res)
#> <fs_result> unsupervised_variance
#> Selected 1 of 2 features
#> a
#> Details: mask, indices, filtered, threshold, direction, action, n_features (in $details)
#>
#> Call:
#> fs_unsupervised(data = data.frame(a = c(1, 5, 2, 8), b = c(1,
#> 1, 1, 1)), method = "variance", threshold = 0.5)
#>
#> Scores (2 features, ranked):
#> feature score selected
#> a 10 *
#> b 0
#> (* = selected)