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Supervised Filter-Based Feature Selection

Source: R/fs_supervised.R
fs_supervised.Rd

Performs supervised, univariate, filter-based feature selection: every column of data except target is scored against the target, and features are selected or dropped by comparing that score to threshold.

Usage

fs_supervised(
  data,
  target,
  method = c("auto", "correlation", "anova"),
  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 containing target and the candidate feature columns. Every column other than target must be numeric. The input is copied, never modified in place.

target

Character. Name of the target column in data. It is removed from the candidate features and used as the scoring target. The target column itself may be numeric, factor, character, or logical; any other type is an error.

method

One of "auto" (default), "correlation", "anova".

threshold

Non-negative, finite numeric scalar threshold applied to the feature scores (not to the target directly). Default 0. Note that the two methods put scores on different scales: [0, 1] for "correlation" and [0, Inf] for "anova".

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), rows with NA in a feature or in the target are dropped when computing that feature's score. If FALSE, an NA anywhere in a feature or the target makes that feature's score undefined.

output

One of "result" (default), "matrix", "dt", "data.frame", "mask", "indices", "names", "list". Throughout, "candidate features" means the columns of data other than target, in their original order.

  • "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, one element per candidate feature, named after the candidate features; TRUE marks a selected column.

  • "indices": integer vector of the selected column indices, indexing the candidate features (that is, data without target), 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_arg (the method as requested), method_used (the method after "auto" was resolved), 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: "supervised_" followed by the resolved scoring method, for example "supervised_correlation".

  • task: "regression" for a numeric target, "classification" for a factor target, NA otherwise.

  • 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) - 1).

  • 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

This is one of the cheapest supervised screens in featR: no predictive model is fitted, each feature is scored on its own, and the cost is linear in the number of columns, so it scales to wide data. The flip side is that scoring is strictly univariate: it cannot see interactions between features, and it will happily keep a whole group of near-duplicate columns that all correlate with the target. Use fs_correlation() to prune that redundancy afterwards, or a wrapper such as fs_recursivefeature() or fs_svm() when the joint contribution is what matters.

Supported methods:

  • "correlation": Absolute Pearson correlation (numeric target), so scores lie in [0, 1]. A constant feature (or target) has no defined correlation and scores NA; the check is relative to the column's magnitude, so features measured on a very small scale are scored normally.

  • "anova": One-way ANOVA F-statistic (categorical target), so scores lie in [0, Inf]: a feature that separates the classes perfectly (no spread within any class) scores Inf, and a constant feature scores NA.

  • "auto": Chooses "correlation" for a numeric target and "anova" for a categorical one. Because the two score scales differ, a message reports the resolved method when verbose = TRUE.

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(
  strong = c(1, 2, 3, 4),
  mirror = c(4, 3, 2, 1),
  weak   = c(1, 0, 1, 0),
  y      = c(1, 2, 3, 4)
)

# Default: an fs_result
res <- fs_supervised(df, target = "y", method = "correlation",
                     threshold = 0.5)
res$selected
#> [1] "strong" "mirror"
res$scores
#>    strong    mirror      weak 
#> 1.0000000 1.0000000 0.4472136 
res$details$filtered
#>    strong mirror
#>     <num>  <num>
#> 1:      1      4
#> 2:      2      3
#> 3:      3      2
#> 4:      4      1

# The classic shapes are still available
fs_supervised(df, target = "y", method = "correlation", threshold = 0.5,
              output = "names")
#> [1] "strong" "mirror"

# ANOVA against a factor target
df_fac <- data.frame(
  wide = c(1, 2, 10, 11),
  mild = c(1, 3, 2, 4),
  grp  = factor(c("a", "a", "b", "b"))
)
fs_supervised(df_fac, target = "grp", method = "anova", threshold = 1,
              output = "matrix")
#>      wide
#> [1,]    1
#> [2,]    2
#> [3,]   10
#> [4,]   11