Which of the following is NOT a good reason to regularize input distributions using a transformation?

Transforming input distributions using regularization can enhance the interpretability of models, making complex relationships clearer and more understandable. However, while ease of interpretation is an advantageous outcome of certain transformations, it is not necessarily a primary reason for implementing regularization techniques.

The motivation to regularize data largely stems from addressing mathematical and statistical limitations inherent in regression models. For instance, regression models are particularly vulnerable to extreme or outlier values, which can considerably impact the accuracy and reliability of predictions. Transformations can help mitigate this sensitivity.

Moreover, high skewness present in input distributions can lead to significant predictive challenges, as it can distort the relationships the model tries to learn. By transforming the data to address skewness, predictions can become more stable and reliable.

Lastly, an improvement in model performance is often directly tied to these transformations, as they can lead to more balanced distributions, reducing bias and enhancing the overall accuracy of the model. Thus, while model interpretation can improve as a result of applying transformations, the other reasons clearly highlight the more fundamental need for regularization in predictive modeling contexts.