Abstract
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.
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Notes
- 1.
The Ames housing dataset, with only 1460 observations, was small enough to performed repeated k-fold CV. For larger datasets, the computational costs of performing repeated k-fold CV might outweigh the benefits of obtaining more accurate estimates of RMSE.
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Nakatsu, R.T. (2024). Explainable AI in Machine Learning Regression: Creating Transparency of a Regression Model. In: Nah, F.FH., Siau, K.L. (eds) HCI in Business, Government and Organizations. HCII 2024. Lecture Notes in Computer Science, vol 14720. Springer, Cham. https://doi.org/10.1007/978-3-031-61315-9_16
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