An interface for variable importance measures for a fitted tidyfit.models frames

A generic method for calculating XAI and variable importance methods for tidyfit.models frames.

Usage

explain(object, use_package = NULL, use_method = NULL, ...)

Arguments

object: model.frame created using regress, classify or m
use_package: the package to use to calculate variable importance. See 'Details' for possible options.
use_method: the method from 'use_package' that should be used to calculate variable importance.
...: additional arguments passed to the importance method

Value

A 'tibble'.

Details

WARNING This function is currently in an experimental stage.

The function uses the 'model_object' column in a tidyfit.model frame to return variable importance measures for each model.

Possible packages and methods include:

`sensitivity` package:

The package provides methods to assess variable importance in linear regressions ('lm') and classifications ('glm').

Usage: use_package="sensitivity" Methods:

"lmg" (Shapley regression),
"pmvd" (Proportional marginal variance decomposition),
"src" (standardized regression coefficients),
"pcc" (partial correlation coefficients),
"johnson" (Johnson indices)

See ?sensitivity::lmg for more information and additional arguments.

`iml` package:

Integration with iml is currently in progress. The methods can be used for 'nnet', 'rf', 'lasso', 'enet', 'ridge', 'adalasso', 'glm' and 'lm'.

Usage: use_package="iml" Methods:

"Shapley" (SHAP values)
"LocalModel" (LIME)
"FeatureImp" (Permutation-based feature importance)

The argument 'which_rows' (vector of integer indexes) can be used to explain specific rows in the data set for Shapley and LocalModel methods.

`randomForest` package:

This uses the native importance method of the randomForest package and can be used with 'rf' and 'quantile_rf' regression and classification.

Usage: use_package="randomForest" Methods:

"mean_decrease_accuracy"

References

Molnar C, Bischl B, Casalicchio G (2018). “iml: An R package for Interpretable Machine Learning.” JOSS, 3(26), 786. doi:10.21105/joss.00786.

Iooss B, Veiga SD, Janon A, Pujol G, Broto wcfB, Boumhaout K, Clouvel L, Delage T, Amri RE, Fruth J, Gilquin L, Guillaume J, Herin M, Idrissi MI, Le Gratiet L, Lemaitre P, Marrel A, Meynaoui A, Nelson BL, Monari F, Oomen R, Rakovec O, Ramos B, Rochet P, Roustant O, Sarazin G, Song E, Staum J, Sueur R, Touati T, Verges V, Weber F (2024). sensitivity: Global Sensitivity Analysis of Model Outputs and Importance Measures. R package version 1.30.0, https://CRAN.R-project.org/package=sensitivity.

A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18--22.

Author

Johann Pfitzinger

Examples

data <- dplyr::group_by(tidyfit::Factor_Industry_Returns, Industry)
fit <- regress(data, Return ~ ., m("lm"), .mask = "Date")
explain(fit, use_package = "sensitivity", use_method = "src")
#> Registered S3 method overwritten by 'sensitivity':
#>   method    from 
#>   print.src dplyr
#> # A tibble: 60 × 4
#> # Groups:   Industry, model [10]
#>    Industry model term   importance
#>    <chr>    <chr> <chr>       <dbl>
#>  1 Durbl    lm    Mkt-RF    0.830  
#>  2 Durbl    lm    SMB       0.0831 
#>  3 Durbl    lm    HML       0.119  
#>  4 Durbl    lm    RMW       0.0679 
#>  5 Durbl    lm    CMA       0.0665 
#>  6 Durbl    lm    RF       -0.00154
#>  7 Enrgy    lm    Mkt-RF    0.739  
#>  8 Enrgy    lm    SMB      -0.0278 
#>  9 Enrgy    lm    HML       0.162  
#> 10 Enrgy    lm    RMW       0.0572 
#> # ℹ 50 more rows

data <- dplyr::filter(tidyfit::Factor_Industry_Returns, Industry == Industry[1])
fit <- regress(data, Return ~ ., m("lm"), .mask = c("Date", "Industry"))
explain(fit, use_package = "iml", use_method = "Shapley", which_rows = c(1))
#> # A tibble: 6 × 5
#> # Groups:   model [1]
#>   model term   importance phi.var feature.value
#>   <chr> <chr>       <dbl>   <dbl> <chr>        
#> 1 lm    Mkt-RF    -0.923  19.4    Mkt.RF=-0.39 
#> 2 lm    SMB       -0.0493  0.0628 SMB=-0.44    
#> 3 lm    HML        0.0301  0.0241 HML=-0.89    
#> 4 lm    RMW        0.142   1.87   RMW=0.68     
#> 5 lm    CMA       -0.439   0.494  CMA=-1.23    
#> 6 lm    RF        -0.173   0.323  RF=0.27