Grouped variable importance with random forests and application to multiple functional data analysis

DOI10.1016/j.csda.2015.04.002zbMath1468.62069arXiv1411.4170OpenAlexW2097296149MaRDI QIDQ1663198

Baptiste Gregorutti, Philippe Saint-Pierre, Bertrand Michel

Publication date: 21 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1411.4170

zbMATH Keywords

random forestsfunctional data analysisgroup variable selectiongroup permutation importance measure

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Functional data analysis (62R10) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (14)

Functional archetype and archetypoid analysis ⋮ Grouped feature importance and combined features effect plot ⋮ Supervised classification of curves via a combined use of functional data analysis and tree-based methods ⋮ Understanding complex predictive models with ghost variables ⋮ Trees, forests, and impurity-based variable importance in regression ⋮ Multivariate analysis of variance for functional data ⋮ All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously ⋮ Testing conditional independence in supervised learning algorithms ⋮ Classification tree algorithm for grouped variables ⋮ Functional variable selection via Gram–Schmidt orthogonalization for multiple functional linear regression ⋮ Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance ⋮ Quantifying the closeness to a set of random curves via the mean marginal likelihood ⋮ Random forest-based approach for physiological functional variable selection for driver's stress level classification ⋮ Comments on ``Data science, big data and statistics

Uses Software

• fda (R)
• GeneSrF
• wmtsa
• reccv

Cites Work

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• Ideal spatial adaptation by wavelet shrinkage
• 10.1162/153244303322753616
• Functional Classification with Margin Conditions
• Model Selection and Estimation in Regression with Grouped Variables
• Linear Statistical Inference and its Applications
• Random forests
• Gene selection for cancer classification using support vector machines

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