Which of the following is not a good reason to "regularize" input distributions using a simple transformation?

Regularizing input distributions is often employed to enhance model performance and interpretation. The act of transforming input distributions can help mitigate the influence of extreme values, which indeed makes models more robust. Additionally, it addresses the issue of skewness in the data, ensuring that the selection of model inputs is based on informative features rather than those distorted by outliers.

While regularization can assist with model interpretation, particularly in making complex models more understandable, it is not primarily aimed at simplifying interpretation by itself. The primary motivations for transformation include improving model robustness (by managing sensitivity to outliers), facilitating the selection of better input distributions, and enhancing overall model performance. Therefore, it is less accurate to categorize improved ease of interpretation as one of the foundational reasons for regularization when compared to these other more impactful benefits.

In summary, while interpreting models might be easier in some cases following a transformation, it is not as significant a motivation for performing regularization compared to the other options listed.