Linear regression algorithms fit a straight line, or another function that is linear in its parameters such as a polynomial, to numeric data, typically by performing matrix inversions to minimize ...

Other common machine learning regression algorithms (short of neural networks) include Naive Bayes, Decision Tree, K-Nearest Neighbors, LVQ (Learning Vector Quantization), LARS Lasso, Elastic Net ...

EHR data may be particularly suitable for machine learning (ML) techniques, as such algorithms can process high-dimensional data and capture nonlinear relationships between variables. By comparison, ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the AdaBoost.R2 algorithm for regression problems (where the goal is to predict a single numeric value). The ...

This paper develops some efficient algorithms for linear spline and piecewise multiple linear regression. A plotting procedure that shows the existence and location of changes in linear regression ...

Richard L. Dykstra, An Algorithm for Restricted Least Squares Regression, Journal of the American Statistical Association, Vol. 78, No. 384 (Dec., 1983), pp. 837-842 ...