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

Source: R/explain.tidyfit.models.R
explain.tidyfit.models.Rd

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

Usage

# S3 method for class 'tidyfit.models'
explain(
  object,
  use_package = NULL,
  use_method = NULL,
  ...,
  .keep_grid_id = FALSE
)

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

.keep_grid_id

boolean. By default the grid ID column is dropped, if there is only one unique setting per model or group. .keep_grid_id = TRUE ensures that the column is never dropped.

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"

partimp package:

This is currently still experimental. Documentation will be provided when the package is more stable.

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")
#> # 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    -0.520  13.0    Mkt.RF=-0.39 
#> 2 lm    SMB       -0.0977  0.0488 SMB=-0.44    
#> 3 lm    HML        0.0460  0.0188 HML=-0.89    
#> 4 lm    RMW        0.232   1.69   RMW=0.68     
#> 5 lm    CMA       -0.567   0.503  CMA=-1.23    
#> 6 lm    RF        -0.175   0.192  RF=0.27