Interpreting Linear Beta Coefficients Alongside Feature Importances in Machine Learning

Machine-learning regression models lack the interpretability of their conventional linear counterparts. Tree- and forest-based models offer feature importances, a vector of probabilities indicating the impact of each predictive variable on a model’s results. This brief note describes how to interpret the beta coefficients of the corresponding linear model so that they may be compared directly to feature importances in machine learning

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Year of publication:	[2021]
Authors:	Chen, James Ming
Publisher:	[S.l.] : SSRN
Subject:	Künstliche Intelligenz \| Artificial intelligence \| Betafaktor \| Beta risk \| Theorie \| Theory \| Portfolio-Management \| Portfolio selection

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