Visualization and exploration of high-dimensional functions using the functional anova decomposition

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Visualizing the effects of predictor variables in black box supervised learning models (2016, background): “ors on black box supervised learning models, the correlation orthogonality in other functional ANOVA decompositions may be less relevant and useful than the ALE orthogonalitypseudo- . As discussed in Roosen (1995), the if predictors are dependent , it may even be preferable to artificially impose a ”
PREDICTIVE LEARNING VIA RULE ENSEMBLES (2008, methodology): “Roosen (1995), Owen (2001) Jiang and Owen (2001) study interaction eﬀects in “black-box” models using the functional ANOVA decomposition of F ( x ) and product measure.”
Generalized Functional ANOVA Diagnostics for High-Dimensional Functions of Dependent Variables (2007, methodology): “The functional ANOVA was employed with a uniform distribution by Jiang and Owen (2003) and Roosen (1995) to provide bivariate plots of low-dimensional effects.”
Discovering additive structure in black box functions (2004, background): “The remaining variables all have interactions with “lstat” apart from “b.” These are very similar ﬁndings to those [13].”