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Variable Importance Using Decision Trees

NeurIPS 2017 · Learning theory and algorithms › Prediction and decision losses · approach: Optimal decision tree learning · 61 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use DSTUMP instead of Lasso/SIS under linear uncorrelated designSIS/Lasso · simulated linear model, uncorrelated designsupport recovery fractionHurt
use DSTUMP instead of SIS under mildly correlated designSIS · simulated linear model, correlated designsupport recovery fractionNo clear effect
use DSTUMP instead of SIS and Lasso with exponential nonlinearitySIS/Lasso · simulated additive exp modelsupport recovery fractionHelped
use TREEWEIGHT (multi-level trees) instead of DSTUMP with interaction termsDSTUMP · simulated interaction modelssupport recovery fractionHelped
use impurity-based TREEWEIGHT instead of permutation importanceTREEWEIGHT-permutation · simulated modelssupport recovery fractionHelped

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