Skip to contents

Summarize a featR result

Source: R/fs-result.R
summary.fs_result.Rd

Prints the result, the call that produced it, and a table of scores ranked strongest first.

Usage

# S3 method for class 'fs_result'
summary(object, n = 20L, ...)

Arguments

object

An fs_result object.

n

Maximum number of rows in the score table. Default 20.

...

Unused, for consistency with the generic.

Value

object, invisibly. Called for the printed output.

Details

The output starts with everything print() shows, then the recorded call. For methods that produce per-feature scores it ends with a table of every scored feature, with columns feature, score (rounded to four significant digits), and selected (* marks the kept features). The table is truncated to n rows with a "... (m more)" tail.

How rows are ranked

For most methods a larger score means a stronger feature, so rows are in decreasing order of score. A negative importance (a feature that did worse than its permuted copy) therefore ranks below every positive one. The other rules are:

  • fs_lasso() scores are signed standardized coefficients. The sign gives the direction of the effect, not its strength, so rows are in decreasing order of absolute value.

  • fs_chi() scores are adjusted p-values, where smaller is stronger, so rows are in increasing order and the most significant feature comes first.

  • fs_unsupervised() and fs_supervised() also use increasing order when they kept the low side of the threshold (direction = "below" with action = "keep", or "above" with "remove"), so the kept features come first.

Two methods select by something other than a cut on the score. For them the selected features are listed first, each group ordered by score, so a kept feature is never ranked below, or truncated out behind, a dropped one:

  • fs_mars() selects every predictor the final earth model uses. A used predictor can have a zero or negative GCV importance while pruned predictors score 0.

  • fs_correlation() with prune = TRUE scores each variable by its largest absolute correlation with any other (larger means more redundant) and keeps one member of each redundant pair. Rows are in increasing order within each group, least redundant first. With prune = FALSE the selection is the redundant set itself, and rows are in decreasing order of score.

When the result records a per-feature keep-mask (details$mask, as the threshold filters do), the selected marks are matched by position, so two features sharing a name are marked separately. Otherwise they are matched by name.

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)