AndreasChristianHill/classoptimr: Optimal classification schemes for prediction models with continuous response variable

Core of this package is a heuristic optimization procedure (Simulated Annealing) that allows for identifying optimal classification schemes for prediction models with continuous response variables. The implemented methods were primarily developed to quantify the classification accuracy of prediction maps based on statistical models that provide predictions on a continuous scale. In many cases, these continuous predictions are afterwards discretized into classes for better visualization purposes without considering the resulting accuracies of the classification scheme. In a more general context, the optimization method can also be used to detect non-constant prediction performance of statistical models.

Getting started

Package details

MaintainerAndreas Hill <[email protected]>
LicenseGPL (>= 2)
Version0.1.0
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("AndreasChristianHill/classoptimr")
AndreasChristianHill/classoptimr documentation built on Nov. 19, 2017, 12:21 a.m.