Title: Feature Selection Using Supervised Filter-Based Methods
Version: 0.1.0
Description: Tidy tools to apply filter-based supervised feature selection methods. These methods score and rank feature relevance using metrics such as p-values, correlation, and importance scores (Kuhn and Johnson (2019) <doi:10.1201/9781315108230>).
License: MIT + file LICENSE
URL: https://github.com/tidymodels/filtro
BugReports: https://github.com/tidymodels/filtro/issues
Depends: R (≥ 4.1)
Imports: purrr, rlang (≥ 1.1.0), stats, tibble
Suggests: aorsf, dplyr, FSelectorRcpp, modeldata, partykit, ranger, testthat (≥ 3.0.0), titanic
Config/Needs/website: tidyverse/tidytemplate
Config/testthat/edition: 3
Encoding: UTF-8
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-07-15 16:22:14 UTC; franceslin
Author: Frances Lin [aut, cre], Max Kuhn [aut], Emil Hvitfeldt [aut], Posit Software, PBC ROR ID [cph, fnd]
Maintainer: Frances Lin <franceslinyc@gmail.com>
Repository: CRAN
Date/Publication: 2025-07-18 15:20:24 UTC

filtro: Feature Selection Using Supervised Filter-Based Methods

Description

Tidy tools to apply filter-based supervised feature selection methods. These methods score and rank feature relevance using metrics such as p-values, correlation, and importance scores (Kuhn and Johnson (2019) doi:10.1201/9781315108230).

Author(s)

Maintainer: Frances Lin franceslinyc@gmail.com

Authors:

Other contributors:

See Also

Useful links:

Compute F-statistic and p-value scores using ANOVA F-test

Description

Evaluate the relationship between a numeric outcome and a categorical predictor, or vice versa, by computing the ANOVA F-statistic or p-value. Output a tibble result with with one row per predictor, and four columns: name, score, predictor, and outcome.

Usage

get_scores_aov(score_obj, data, outcome)

Arguments

score_obj

A score object. See score_aov() for details.

data

A data frame or tibble containing the outcome and predictor variables.

outcome

A character string specifying the name of the outcome variable.

Details

The score_obj object may include the following components:

neg_log10

A logical value indicating whether to apply a negative log10 transformation to p-values (default is TRUE).

  • If TRUE, p-values are transformed as -log10(pval). In this case:

    • The default fallback_value is Inf

    • The default direction is "maximize"

  • If FALSE, raw p-values are used. In this case:

    • The fallback_value should be set to 0

    • The direction should be set to "minimize"

Value

A tibble of result with one row per predictor, and four columns:

Examples

data(ames, package = "modeldata")
data <- modeldata::ames |>
  dplyr::select(
    Sale_Price,
    MS_SubClass,
    MS_Zoning,
    Lot_Frontage,
    Lot_Area,
    Street
  )
# Define outcome
outcome <- "Sale_Price"
# Create a score object
score_obj <- score_aov()
score_res <- get_scores_aov(score_obj, data, outcome)
score_res
# Change score type
score_obj$score_type <- "pval"
score_res <- get_scores_aov(score_obj, data, outcome)
score_res
# Use raw p-values instead of -log10(p-values)
score_obj$score_type <- "pval"
score_obj$neg_log10 <- FALSE
score_obj$direction <- "minimize"
score_obj$fallback_value <- 0
score_res <- get_scores_aov(score_obj, data, outcome)
score_res

Construct a new score object

Description

Create a new score object that contains associated metadata, such as range, fallback_value, score_type, direction, and other relevant attributes.

Usage

new_score_obj(
  subclass = c("cat_num", "cat_cat", "num_num", "any"),
  outcome_type = c("numeric", "factor"),
  predictor_type = c("numeric", "factor"),
  case_weights = NULL,
  range = NULL,
  inclusive = NULL,
  fallback_value = NULL,
  score_type = NULL,
  trans = NULL,
  sorts = NULL,
  direction = NULL,
  deterministic = NULL,
  tuning = NULL,
  ties = NULL,
  calculating_fn = NULL,
  label = NULL,
  ...
)

Arguments

subclass

A character string indicating the type of predictor-outcome combination the scoring method supports. One of:

  • "cat_num"

  • "cat_cat"

  • "num_num"

  • "any"

outcome_type

A character string indicating the outcome type. One of:

  • "numeric"

  • "factor"

predictor_type

A character string indicating the predictor type. One of:

  • "numeric"

  • "factor"

case_weights

A logical value, indicating whether the model accepts case weights (TRUE) or not (FALSE).

range

A numeric vector of length two, specifying the minimum and maximum possible values, respectively.

inclusive

A logical vector of length two, indicating whether the lower and upper bounds of the range are inclusive (TRUE) or exclusive (FALSE).

fallback_value

A numeric scalar used as a fallback value. Typical values include:

  • 0

  • 1

  • Inf

score_type

A character string indicating the type of scoring metric to compute. Available options include:

  • ANOVA F-Test: "fstat", "pval"

  • Correlation: "pearson", "spearman"

  • Cross Tabulation: "pval_chisq", "pval_fisher"

  • Random Forest:"imp_rf", "imp_rf_conditional", "imp_rf_oblique"

  • Information Gain: "infogain", "gainratio", "symuncert"

  • ROC AUC: "roc_auc"

trans

Currently not used.

sorts

An optional function used to sort the scores. Common options include:

  • identity

  • abs

  • function(score) max(score, 1 - score)

direction

A character string indicating the optimization direction. One of:

  • "maximize"

  • "minimize"

  • "target"

deterministic

A logical value, indicating whether the score is deterministic (TRUE) or not (FALSE).

tuning

A logical value, indicating whether the model should be tuned (TRUE) or not (FALSE).

ties

An optional logical value indicating whether ties in score can occur (TRUE) or not (FALSE).

calculating_fn

An optional function used to compute the score. A default function is selected based on the score_type.

label

A named character string that can be used for printing and plotting.

...

Currently not used.

Value

A score object.

Examples

# Create a score object
new_score_obj()

Create a score object for ANOVA F-test F-statistics and p-values

Description

Construct a score object containing metadata for univariate feature scoring using the ANOVA F-test. Output a score object containing associated metadata such as range, fallback_value, score_type ("fstat" or "pval"), direction, and other relevant attributes.

Usage

score_aov(
  range = c(0, Inf),
  fallback_value = Inf,
  score_type = "fstat",
  direction = "maximize"
)

Arguments

range

A numeric vector of length two, specifying the minimum and maximum possible values, respectively.

fallback_value

A numeric scalar used as a fallback value. Typical values include:

  • 0

  • Inf (default)

For F-statistics, the fallback_value should be "Inf". For p-values, since the default applies a negative log10 transformation to p-values, the fallback_value should be "Inf".

score_type

A character string indicating the type of scoring metric to compute. Available options include:

  • "fstat"

  • "pval"

direction

A character string indicating the optimization direction. One of:

  • "maximize" (default)

  • "minimize"

  • "target"

For F-statistics, the direction should be "maximize". For p-values, since the default applies a negative log10 transformation to p-values, the direction should be "maximize".

Value

A score object containing associated metadata such as range, fallback_value, score_type ("fstat" or "pval"), direction, and other relevant attributes.

Examples

# Create a score object
score_aov()
# Change score type
score_obj <- score_aov()
score_obj$score_type <- "pval"