Unsupervised Filter-Based Feature Selection — fs

Performs unsupervised, univariate, filter-based feature selection by scoring each feature using a chosen unsupervised criterion and selecting or dropping features based on a threshold on that score.

Usage

fs_unsupervised(
  data,
  method = c("variance", "mad", "iqr", "range", "missing_prop", "n_unique"),
  threshold = 0,
  direction = c("above", "below"),
  action = c("keep", "remove"),
  include_equal = FALSE,
  na_rm = TRUE,
  output = c("result", "matrix", "dt", "data.frame", "mask", "indices", "names", "list"),
  verbose = FALSE
)

Arguments

data

A data.frame, data.table, or matrix; all columns must be numeric. Every column is a candidate feature. The input is copied, never modified in place.

method

One of "variance" (default), "mad", "iqr", "range", "missing_prop", "n_unique".

threshold

Non-negative, finite numeric scalar threshold applied to the feature scores. Default 0. The scales differ by method (a variance is in squared units, "missing_prop" is in [0, 1], "n_unique" is a count), so a threshold is not portable between methods.

direction

One of "above" (default), "below"; compares scores to threshold.

action

One of "keep" (default), "remove"; determines whether features meeting the condition are retained or dropped.

include_equal

Logical; if TRUE, comparisons are inclusive (greater/less than or equal) instead of strict. Default FALSE.

na_rm

Logical; if TRUE (the default), remove NAs when computing scores. Has no effect on "missing_prop" and "n_unique", which always account for NAs the same way.

output

One of "result" (default), "matrix", "dt", "data.frame", "mask", "indices", "names", "list".

"result" (default): an fs_result object (see Value).
"matrix": numeric matrix of the selected features.
"dt": data.table of the selected features.
"data.frame": data.frame of the selected features.
"mask": logical vector of length ncol(data), named after the columns of data; TRUE marks a selected column.
"indices": integer vector of the selected column indices, named after the selected columns.
"names": character vector of the selected column names.
"list": list with components filtered (matrix), mask, indices, names, scores (all as above), and meta, a list recording method, threshold, direction, action, include_equal, na_rm, n_input_cols, and n_kept_cols.

verbose

Logical; emit progress messages. Default FALSE.

Value

With the default output = "result", an object of class fs_result with elements:

selected: character vector of selected feature names.
scores: named numeric vector of per-feature scores, in column order, NA where the score is undefined.
method: "unsupervised_" followed by the scoring method, for example "unsupervised_variance".
task: NA_character_ (unsupervised selection has no task).
model: NULL.
details: a list holding, in this order, mask (the logical keep-mask over the candidate features), indices (the selected column indices, named after the selected columns), filtered (the selected columns as a data.table), threshold, direction, action, and n_features (the number of candidate features, that is ncol(data)).
call: the matched call.

Any other output returns that shape instead, exactly as documented above. When no feature meets the selection criteria, a warning is issued and the tabular shapes come back empty: "matrix" and "data.frame" have zero columns and keep the input row count, while the "dt" shape (and details$filtered) is only guaranteed to have zero columns – data.table represents a zero-column table as having zero rows, so its row count is not preserved.

Details

No target is involved, so this is the right tool for the first cleaning pass – dropping constant or near-constant columns, columns that are mostly missing, or columns with too few distinct values – and it is safe to run before a train/test split, since nothing about the outcome informs it. It says nothing about whether a feature is useful: a high-variance column can be pure noise, and a low-variance one can be the best predictor you have. Use fs_supervised() or a model-based method for that judgment.

Supported methods:

"variance": Sample variance (denominator n - 1).
"mad": Median absolute deviation, computed with stats::mad()'s default consistency constant 1.4826 (normal-consistent). Scores are therefore on the standard-deviation (sigma) estimate scale, not the raw median-absolute-deviation scale, and thresholds should be chosen accordingly.
"iqr": Interquartile range.
"range": Max - Min.
"missing_prop": Proportion of missing values, in [0, 1]. Note that with the default threshold = 0, direction = "above", and action = "keep", this KEEPS the features with the most missing values, which is almost never the intent; a warning suggests action = "remove" or direction = "below". The warning fires only when all three of threshold, direction, and action are left at their defaults, so passing any one of them explicitly opts out of the advisory.
"n_unique": Number of unique non-NA values.

na_rm affects only the methods that summarize the observed values ("variance", "mad", "iqr", "range"); "missing_prop" and "n_unique" always look at the whole column and treat NA as NA.

Features whose score is undefined (NA) are never selected, under both action = "keep" and action = "remove"; a warning reports how many such features were excluded.

Columns are subset by integer index, never by name, so duplicated column names cannot select the wrong columns.

Examples

df <- data.frame(
  spread = c(1, 2, 3, 4, 100),
  flat   = c(2, 2, 2, 2, 2),
  gappy  = c(1, NA, 3, NA, 5)
)

# Default: an fs_result
res <- fs_unsupervised(df, method = "variance", threshold = 0.5)
res$selected
#> [1] "spread" "gappy" 
res$scores
#> spread   flat  gappy 
#> 1902.5    0.0    4.0 
res$details$filtered
#>    spread gappy
#>     <num> <num>
#> 1:      1     1
#> 2:      2    NA
#> 3:      3     3
#> 4:      4    NA
#> 5:    100     5

# The classic shapes are still available
fs_unsupervised(df, method = "variance", threshold = 0.5,
                output = "matrix")
#>      spread gappy
#> [1,]      1     1
#> [2,]      2    NA
#> [3,]      3     3
#> [4,]      4    NA
#> [5,]    100     5

# Remove features with missing proportion >= 0.2
fs_unsupervised(df, method = "missing_prop", threshold = 0.2,
                direction = "above", action = "remove",
                include_equal = TRUE, output = "names")
#> [1] "spread" "flat"