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Compute the feature level contributions for a caret_stack.

Source: R/caret_stack.R
compute_feature_contributions.caret_stack.Rd

Computes the contribution of each individual feature to the ensemble's predictions using a two-stage application of caret::varImp:

  1. Dataset-level weights: varImp is applied to the ensemble meta-learner, treating each base model's predictions as a feature. This yields a relative importance weight for each dataset.

  2. Feature-level importance: varImp is applied to each base model individually, yielding feature importance scores within each dataset.

The final contribution of a feature is the product of its dataset-level weight and its within-dataset feature importance score. All scores are normalized to sum to 100.

Usage

# S3 method for class 'caret_stack'
compute_feature_contributions(object, n_features = 20, ...)

Arguments

object

A caret_stack object.

n_features

The maximum number of features to include. Setting to a very large value will include all features. Default is 20.

...

Not used. Included for S3 compatibility.

Value

A data.table

Examples

# Load pre-trained example caret_stack object
data(heart_failure_stack)

compute_feature_contributions(heart_failure_stack)
#>        Model              Feature Relative Contribution
#>       <char>               <char>                 <num>
#>  1:   holter HS_HR_AVE_DayToNight             41.585315
#>  2:     mrna                 TLR7              7.210145
#>  3:     mrna                 PRLR              5.048845
#>  4:     mrna               FLVCR2              4.168204
#>  5: proteins                ITIH2              4.114475
#>  6:   holter    HR_SECONDS_MAX_RR              2.516984
#>  7:     mrna               SLC8A1              2.398912
#>  8:     mrna              RETREG1              2.088714
#>  9:     mrna                 TTC9              2.064062
#> 10: proteins                  PLG              1.965363
#> 11:     mrna                FOLR2              1.749651
#> 12:     mrna                 SVBP              1.738274
#> 13:     mrna                 NT5E              1.614556
#> 14:     mrna                 ERFE              1.390788
#> 15: proteins                 PON3              1.248055
#> 16: proteins                  B2M              1.200775
#> 17: proteins             SERPINA4              1.065383
#> 18: proteins                 CST3              1.047414
#> 19: proteins                 ANGT              1.039845
#> 20:     mrna             ANKRD36B              1.029405
#>        Model              Feature Relative Contribution
#>       <char>               <char>                 <num>