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Simulate data as in Ewald et al. (2024)

Source: R/data-sim.R
sim_dgp_ewald.Rd

Reproduces the data generating process from Ewald et al. (2024) for benchmarking feature importance methods. Includes correlated features and interaction effects.

Usage

sim_dgp_ewald(n = 500)

Arguments

n

(integer(1)) Number of samples to create.

Value

A regression task (mlr3::TaskRegr) with data.table backend.

Details

Mathematical Model: $$X_1, X_3, X_5 \sim \text{Uniform}(0,1)$$ $$X_2 = X_1 + \varepsilon_2, \quad \varepsilon_2 \sim N(0, 0.001)$$ $$X_4 = X_3 + \varepsilon_4, \quad \varepsilon_4 \sim N(0, 0.1)$$ $$Y = X_4 + X_5 + X_4 \cdot X_5 + \varepsilon, \quad \varepsilon \sim N(0, 0.1)$$

Feature Properties:

  • X1, X3, X5: Independent uniform(0,1) distributions

  • X2: Nearly perfect copy of X1 (correlation ≈ 0.99)

  • X4: Noisy copy of X3 (correlation ≈ 0.67)

  • Y depends on X4, X5, and their interaction

References

Ewald, Katharina F, Bothmann, Ludwig, Wright, N. M, Bischl, Bernd, Casalicchio, Giuseppe, König, Gunnar (2024). “A Guide to Feature Importance Methods for Scientific Inference.” In Longo, Luca, Lapuschkin, Sebastian, Seifert, Christin (eds.), Explainable Artificial Intelligence, 440–464. ISBN 978-3-031-63797-1, doi:10.1007/978-3-031-63797-1_22 .

Examples

sim_dgp_ewald(100)
#> 
#> ── <TaskRegr> (100x6) ──────────────────────────────────────────────────────────
#> • Target: y
#> • Properties: -
#> • Features (5):
#>   • dbl (5): x1, x2, x3, x4, x5