Leave-One-Covariate-In (LOCI)

Calculates Leave-One-Covariate-In (LOCI) scores. Despite the name, this implementation can leave in one or more features at a time.

Details

LOCI measures feature importance by training models with only each individual feature (or feature subset) and comparing their performance to a featureless baseline model (optimal constant prediction). The importance is calculated as (featureless_model_loss - single_feature_loss). Positive values indicate the feature performs better than the baseline, negative values indicate worse performance.

Super classes

xplainfi::FeatureImportanceMethod -> xplainfi::LeaveOutIn -> LOCI

Methods

Public methods

• LOCI$new()

• LOCI$clone()

Inherited methods

• xplainfi::FeatureImportanceMethod$combine()
• xplainfi::FeatureImportanceMethod$print()
• xplainfi::FeatureImportanceMethod$reset()
• xplainfi::LeaveOutIn$compute()

---

Method new()

Creates a new instance of this R6 class.

Usage

LOCI$new(
  task,
  learner,
  measure,
  resampling = NULL,
  features = NULL,
  iters_refit = 1L,
  obs_loss = FALSE,
  aggregation_fun = median
)

Arguments

task

(mlr3::Task) Task to compute importance for.

learner

(mlr3::Learner) Learner to use for prediction.

measure

(mlr3::Measure) Measure to use for scoring.

resampling

(mlr3::Resampling) Resampling strategy. Defaults to holdout.

features

(character()) Features to compute importance for. Defaults to all features.

iters_refit

(integer(1)) Number of refit iterations per resampling iteration.

obs_loss

(logical(1)) Whether to use observation-wise loss calculation (analogous to LOCO) when supported by the measure. If FALSE (default), uses aggregated scores.

aggregation_fun

(function) Function to aggregate observation-wise losses when obs_loss = TRUE. Defaults to median, analogous to LOCO.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LOCI$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)
task = tgen("friedman1")$generate(n = 200)
loci = LOCI$new(
  task = task,
  learner = lrn("regr.ranger", num.trees = 50),
  measure = msr("regr.mse")
)
#> ℹ No <Resampling> provided, using holdout resampling with default ratio.
loci$compute()
#> Key: <feature>
#>          feature importance
#>           <char>      <num>
#>  1:   important1   3.551881
#>  2:   important2  -6.699716
#>  3:   important3  -4.446604
#>  4:   important4   5.771280
#>  5:   important5  -7.030814
#>  6: unimportant1 -12.061976
#>  7: unimportant2  -4.970430
#>  8: unimportant3 -10.786875
#>  9: unimportant4 -11.829766
#> 10: unimportant5  -6.400764