polyreg | regtools PACKAGE , WHICH WRAPS polyreg | Machine Learning library

polyreg is a R library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. polyreg has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

NOTE: IT IS RECOMMENDED TO USE THE FUNCTIONS qePolyLin() and qePolyLog() IN THE regtools PACKAGE, WHICH WRAPS polyreg. Development of a package to automate formation and evaluation of multivariate polynomial regression models. Motivation: A simpler, equally effective alternative to neural networks. in Polynomial Regression As an Alternative to Neural Nets, by Cheng, Khomtchouk, Matloff and Mohanty, 2018. Other than the various cross-validation functions, the main functions are polyfit() and predict.polyFit(). One can fit either regression or classification models, with an option to perform PCA for dimension reduction on the predictors/features. Example: Programmer/engineer 2000 Census data, Silicon Valley. Built in to the latest version of the regtools package. Install package or download directly here. In the former case, getPE() reads in the dataset and does some preprocessing, producing a data frame pe. Example: Vertebral Column data from the UC Irvine Machine Learning Repository. Various spinal measurements, with three conditions, Normal, Disk Hernia and Spondylolisthesis. Let's predict the conditions. Forward stepwise regression is also available with FSR which also accepts polynomial degree and interaction as inputs. FSR() contains a handful of parameters which make the function more or less 'optimistic' about estimating new models. threshold_include sets the minimum improvment on the best model to include new features (default 0.01 in adjusted R^2^ for continuous outcomes and accuracy for multinomial outcomes, with the same adjustment applied). threshold_estimate, is the treshold to keep adding additional features on the same scale. For categorical outcomes, a linear probability model can also be estimated via Ordinary Least Squares for speed.