What primary advantage does regularizing input distributions provide when modeling?

Regularizing input distributions primarily enhances a model's resistance to outlier effects. Regularization techniques, such as Lasso and Ridge regression, work by imposing a penalty on the size of coefficients in a regression model. This approach helps to stabilize the estimates of the model parameters, particularly in the presence of extreme values or outliers in the data.

Outliers can disproportionately influence the predictions and fitted values of a model, leading to overfitting and decreased generalization to new data. By using regularization, the model becomes less sensitive to these extreme values, thereby allowing it to make more robust predictions that are reflective of the underlying distribution rather than influenced heavily by outliers.

This characteristic of regularization contributes to the stability of the model and often results in better performance on unseen data, although that aspect is more closely tied to predictive performance rather than directly addressing outlier resistance. Other choices like ease of interpretation may be a consequence of regularizing simpler models, but the fundamental benefit of resilience against outliers stands out as a primary advantage when modeling with regularized techniques.