Explainable AI in Machine Learning Regression: Creating Transparency of a Regression Model

This paper explores how to develop machine learning regression models that are more explainable and transparent for the end-user. Explainable regression models can be created by rank-ordering the features of the regression model that contribute most to predictive accuracy. In addition, fitting graphs can be generated that show how the addition of each feature in a regression model incrementally improves predictive accuracy. These information graphics are especially useful in understanding the tradeoffs involved in selecting a model that considers both model complexity and model performance. These methods are illustrated with two examples: a multiple regression model using a straightforward application of machine learning regression; and a more complex polynomial regression model that captures higher-order terms and interactions among all variables in the model.