Nonparametric variable importance using an augmented neural network with multi-task learning

ICML 2018 • Jean Feng • Brian Williamson • Noah Simon • Marco Carone

In predictive modeling applications, it is often of interest to determine the relative contribution of subsets of features in explaining the variability of an outcome. It is useful to consider this variable importance as a function of the unknown, underlying data-generating mechanism rather than the specific predictive algorithm used to fit the data... (read more)

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