⚠️ filtro is under active development; breaking changes may occur.
Overview
filtro is tidy tools to apply filter-based supervised feature selection methods. These methods score and rank feature relevance using metrics such as p-values, correlation, feature importance, information gain, and more.
The package provides functions to rank and select a top proportion or number of features using built-in methods and the desirability2 package, and supports streamlined preprocessing, either standalone or within tidymodels workflows such as the recipes package.
Installation
Install the released version of filtro from CRAN with:
install.packages("filtro")Install the development version from GitHub with:
# install.packages("pak")
pak::pak("tidymodels/filtro")Feature selection methods
Currently, the implemented filters include:
ANOVA F-test
Correlation
Random forest feature importance
Information gain
Area under the ROC curve
Cross tabulation (Chi-squared test and Fisher’s exact test)
A scoring example
ames_subset <- modeldata::ames |>
# Use a subset of data for demonstration
dplyr::select(
Sale_Price,
MS_SubClass,
MS_Zoning,
Lot_Frontage,
Lot_Area,
Street
)
ames_subset <- ames_subset |>
dplyr::mutate(Sale_Price = log10(Sale_Price))
# ANOVA p-value
ames_aov_pval_res <-
score_aov_pval |>
fit(Sale_Price ~ ., data = ames_subset)
ames_aov_pval_res@results
#> # A tibble: 5 × 4
#> name score outcome predictor
#> <chr> <dbl> <chr> <chr>
#> 1 aov_pval 237. Sale_Price MS_SubClass
#> 2 aov_pval 130. Sale_Price MS_Zoning
#> 3 aov_pval NA Sale_Price Lot_Frontage
#> 4 aov_pval NA Sale_Price Lot_Area
#> 5 aov_pval 5.75 Sale_Price Street
# Pearson correlation
ames_cor_pearson_res <-
score_cor_pearson |>
fit(Sale_Price ~ ., data = ames_subset)
ames_cor_pearson_res@results
#> # A tibble: 5 × 4
#> name score outcome predictor
#> <chr> <dbl> <chr> <chr>
#> 1 cor_pearson NA Sale_Price MS_SubClass
#> 2 cor_pearson NA Sale_Price MS_Zoning
#> 3 cor_pearson 0.165 Sale_Price Lot_Frontage
#> 4 cor_pearson 0.255 Sale_Price Lot_Area
#> 5 cor_pearson NA Sale_Price Street
# Forest importance
ames_imp_rf_reg_res <-
score_imp_rf |>
fit(Sale_Price ~ ., data = ames_subset, seed = 42)
ames_imp_rf_reg_res@results
#> # A tibble: 5 × 4
#> name score outcome predictor
#> <chr> <dbl> <chr> <chr>
#> 1 imp_rf 0.0144 Sale_Price MS_SubClass
#> 2 imp_rf 0.0102 Sale_Price MS_Zoning
#> 3 imp_rf 0.00693 Sale_Price Lot_Frontage
#> 4 imp_rf 0.0144 Sale_Price Lot_Area
#> 5 imp_rf 0.0000308 Sale_Price Street
# Information gain
ames_info_gain_reg_res <-
score_info_gain |>
fit(Sale_Price ~ ., data = ames_subset)
ames_info_gain_reg_res@results
#> # A tibble: 5 × 4
#> name score outcome predictor
#> <chr> <dbl> <chr> <chr>
#> 1 infogain 0.266 Sale_Price MS_SubClass
#> 2 infogain 0.113 Sale_Price MS_Zoning
#> 3 infogain 0.146 Sale_Price Lot_Frontage
#> 4 infogain 0.140 Sale_Price Lot_Area
#> 5 infogain 0.00365 Sale_Price StreetA filtering exmple for score singular
There are several methods for score singular:
show_best_score_prop()shows best score, based on proportion of predictors.show_best_score_num()shows best score, based on number of predictors.show_best_score_cutoff()shows best score, based on based on cutoff value.show_best_score_dual()shows best score, based on number or proportion of predictors with optional cutoff value.
For one example:
ames_aov_pval_res@results
#> # A tibble: 5 × 4
#> name score outcome predictor
#> <chr> <dbl> <chr> <chr>
#> 1 aov_pval 237. Sale_Price MS_SubClass
#> 2 aov_pval 130. Sale_Price MS_Zoning
#> 3 aov_pval NA Sale_Price Lot_Frontage
#> 4 aov_pval NA Sale_Price Lot_Area
#> 5 aov_pval 5.75 Sale_Price Street
# Show best score, based on proportion of predictors
ames_aov_pval_res |> show_best_score_prop(prop_terms = 0.2)
#> # A tibble: 1 × 4
#> name score outcome predictor
#> <chr> <dbl> <chr> <chr>
#> 1 aov_pval 237. Sale_Price MS_SubClassThere is an option to fill safe value first, and then show best score using various methods.
# Fill safe value
ames_aov_pval_res <- ames_aov_pval_res |> fill_safe_value()
# Show best score, based on proportion of predictors
ames_aov_pval_res |> show_best_score_prop(prop_terms = 0.2)
#> # A tibble: 2 × 4
#> name score outcome predictor
#> <chr> <dbl> <chr> <chr>
#> 1 aov_pval Inf Sale_Price Lot_Frontage
#> 2 aov_pval Inf Sale_Price Lot_AreaA filtering example for scores plural
There are several methods for score plural:
# Create a list
class_score_list <- list(
ames_cor_pearson_res,
ames_imp_rf_reg_res,
ames_info_gain_reg_res
)
# Fill safe values
ames_scores_results <- class_score_list |>
fill_safe_values() |>
# Remove outcome
dplyr::select(-outcome)
ames_scores_results
#> # A tibble: 5 × 4
#> predictor cor_pearson imp_rf infogain
#> <chr> <dbl> <dbl> <dbl>
#> 1 MS_SubClass 1 0.0144 0.266
#> 2 MS_Zoning 1 0.0102 0.113
#> 3 Lot_Frontage 0.165 0.00693 0.146
#> 4 Lot_Area 0.255 0.0144 0.140
#> 5 Street 1 0.0000308 0.00365For one example:
# Single and multi-parameter optimization using desirability functions
ames_scores_results |>
show_best_desirability_prop(
maximize(cor_pearson, low = 0, high = 1)
)
#> # A tibble: 5 × 6
#> predictor cor_pearson imp_rf infogain .d_max_cor_pearson .d_overall
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 MS_SubClass 1 0.0144 0.266 1 1
#> 2 MS_Zoning 1 0.0102 0.113 1 1
#> 3 Street 1 0.0000308 0.00365 1 1
#> 4 Lot_Area 0.255 0.0144 0.140 0.255 0.255
#> 5 Lot_Frontage 0.165 0.00693 0.146 0.165 0.165
ames_scores_results |>
show_best_desirability_prop(
maximize(cor_pearson, low = 0, high = 1),
maximize(imp_rf)
)
#> # A tibble: 5 × 7
#> predictor cor_pearson imp_rf infogain .d_max_cor_pearson .d_max_imp_rf
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 MS_SubClass 1 0.0144 0.266 1 1
#> 2 MS_Zoning 1 0.0102 0.113 1 0.705
#> 3 Lot_Area 0.255 0.0144 0.140 0.255 0.994
#> 4 Lot_Frontage 0.165 0.00693 0.146 0.165 0.479
#> 5 Street 1 0.0000308 0.00365 1 0
#> # ℹ 1 more variable: .d_overall <dbl>
ames_scores_results |>
show_best_desirability_prop(
maximize(cor_pearson, low = 0, high = 1),
maximize(imp_rf),
maximize(infogain)
)
#> # A tibble: 5 × 8
#> predictor cor_pearson imp_rf infogain .d_max_cor_pearson .d_max_imp_rf
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 MS_SubClass 1 0.0144 0.266 1 1
#> 2 MS_Zoning 1 0.0102 0.113 1 0.705
#> 3 Lot_Area 0.255 0.0144 0.140 0.255 0.994
#> 4 Lot_Frontage 0.165 0.00693 0.146 0.165 0.479
#> 5 Street 1 0.0000308 0.00365 1 0
#> # ℹ 2 more variables: .d_max_infogain <dbl>, .d_overall <dbl>
ames_scores_results |>
show_best_desirability_prop(
maximize(cor_pearson, low = 0, high = 1),
maximize(imp_rf),
maximize(infogain),
prop_terms = 0.2
)
#> # A tibble: 1 × 8
#> predictor cor_pearson imp_rf infogain .d_max_cor_pearson .d_max_imp_rf
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 MS_SubClass 1 0.0144 0.266 1 1
#> # ℹ 2 more variables: .d_max_infogain <dbl>, .d_overall <dbl>
ames_scores_results |>
show_best_desirability_prop(
target(cor_pearson, low = 0.2, target = 0.255, high = 0.9)
)
#> # A tibble: 5 × 6
#> predictor cor_pearson imp_rf infogain .d_target_cor_pearson .d_overall
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Lot_Area 0.255 0.0144 0.140 1.00 1.00
#> 2 MS_SubClass 1 0.0144 0.266 0 0
#> 3 MS_Zoning 1 0.0102 0.113 0 0
#> 4 Lot_Frontage 0.165 0.00693 0.146 0 0
#> 5 Street 1 0.0000308 0.00365 0 0
ames_scores_results |>
show_best_desirability_prop(
constrain(cor_pearson, low = 0.2, high = 1)
)
#> # A tibble: 5 × 6
#> predictor cor_pearson imp_rf infogain .d_box_cor_pearson .d_overall
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 MS_SubClass 1 0.0144 0.266 1 1
#> 2 MS_Zoning 1 0.0102 0.113 1 1
#> 3 Lot_Area 0.255 0.0144 0.140 1 1
#> 4 Street 1 0.0000308 0.00365 1 1
#> 5 Lot_Frontage 0.165 0.00693 0.146 0 0Contributing
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