The SAS Data Science Blog
Advanced analytics from SAS data scientists
When building models, data scientists and statisticians often talk about penalty, regularization and shrinkage. What do these terms mean and why are they important? According to Wikipedia, regularization "refers to a process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting. This information usually
Ensemble methods are commonly used to boost predictive accuracy by combining the predictions of multiple machine learning models. The traditional wisdom has been to combine so-called “weak” learners. However, a more modern approach is to create an ensemble of a well-chosen collection of strong yet diverse models. Building powerful ensemble models
A previous post, Spatial econometric modeling using PROC SPATIALREG, introduced the SAS/ETS® SPATIALREG procedure and demonstrated its usage to fit both linear and SAR models by using 2013 county-level home value data in North Carolina. In most analysis for spatial econometrics, you rarely know the true model from which your data