When you’re building a machine learning model you’re faced with the bias-variance tradeoff, where you have to find the balance between having a model that: Is very expressive and captures the real ...

Data structures in modern applications frequently combine the necessity of flexible regression techniques handling, for example, non-linear and spatial effects with high dimensional covariate vectors.

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with two-way interactions between predictor variables. Standard linear regression predicts a single numeric value ...

The study departs from conventional mean-based economic forecasting by focusing on quantile prediction, a technique that ...

We consider estimation and variable selection in high-dimensional Cox regression when a prior knowledge of the relationships among the covariates, described by a network or graph, is available. A ...