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arXiv Machine Learning By Youngseo Cho, V. Roshan Joseph

Measuring Variable Importance Using Proportional Inclusive Marginal Effects

Read the original on arXiv Machine Learning →

The paper introduces PRIME, a new variable‑importance measure that addresses two limitations of the existing proportional marginal effects (PME) method: it is applicable to stochastic models and reduces computational cost. PRIME achieves this by employing a model‑free variable‑selection technique to isolate active variables before computing importance. Experiments on synthetic test functions and real datasets demonstrate that PRIME accurately and reliably quantifies variable importance from noisy input‑output data.

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