The goal of a machine learning regression problem is to predict a single numeric value. There are roughly a dozen different regression techniques such as basic linear regression, k-nearest neighbors ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the naive Bayes regression technique, where the goal is to predict a single numeric value. Compared to other ...

Vol. 81, No. 1, Special issue Statistics on non-Euclidean Spaces and Manifolds (February 2019), pp. 83-103 (21 pages) Regression models for size-and-shape analysis are developed, where the model is ...

Bayesian quantile regression and statistical modelling represent a growing paradigm in contemporary data analysis, extending conventional regression by estimating various conditional quantiles rather ...

Linear regression is a powerful and long-established statistical tool that is commonly used across applied sciences, economics and many other fields. Linear regression considers the relationship ...

Bayesian Additive Regression Trees (BART) is a nonparametric ensemble method that models complex relationships by summing a collection of decision trees, each operating as a weak learner. The Bayesian ...

This is the seventh in a series of lecture notes which, if tied together into a textbook, might be entitled “Practical Regression.” The purpose of the notes is to supplement the theoretical content of ...