Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models

27 Dec 2016Daniel W. Apley

When fitting black box supervised learning models (e.g., complex trees, neural networks, boosted trees, random forests, nearest neighbors, local kernel-weighted methods, etc. ), visualizing the main effects of the individual predictor variables and their low-order interaction effects is often important, and partial dependence (PD) plots are the most popular approach for accomplishing this... (read more)

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