This post is the second one in a series that discusses algorithmic and implementation issues about nonlinear regression using Spark. In the previous post we identified a small window for contribution into Spark MLlib by adding methods for nonlinear regression, starting with the definition and implementation of a general nonlinear model. We remind the reader that regression is essentially an …
Nonlinear regression using Spark – Part 1: Nonlinear models
Regression constitutes a very important topic in supervised learning. Its goal is to predict the value of one or more continuous target variables (responses) given the value of a -dimensional vector of input variables (predictors). More specifically, given a training data set comprising of observations , where , together with corresponding target values , the goal is to predict the …