Supervised Numeric-Value Prediction System

A Supervised Numeric-Value Prediction System is a supervised learning system (that applies a supervised point estimation algorithm to solve a supervised numeric value prediction task.

• AKA: Regression Software, Regression Learning System.
• Context:
  • It can (often) be evaluated using a Supervised Numeric Prediction System Evaluation Task.
  • It can range from being a Model-based Supervised Numeric-Value Prediction System to being a Instance-based Supervised Numeric-Value Prediction System.
  • It can range from being a Regression Software Tool to being a Regression Software Package.
• Example(s):
  • a Linear Regression Software.
  • a Regression Tree Learning System.
  • a k-Nearest Neighbor Numeric-Value Prediction Software.
  • …
• Counter-Example(s):
  • a Supervised Class Prediction System, that solves a supervised class prediction task.
  • a Supervised Rank Prediction System, that solves a supervised rank prediction task.
  • a Supervised Range Prediction System, that solves a supervised range prediction task.
  • a Clustering System, that solves an unsupervised clustering task.
• See: Least Squares.

---

---

References

• http://en.wikipedia.org/wiki/List_of_statistical_packages

2011

• http://en.wikipedia.org/wiki/Regression_analysis#Software
  • All major statistical software packages perform least squares regression analysis and inference. Simple linear regression and multiple regression using least squares can be done in some spreadsheet applications and on some calculators. While many statistical software packages can perform various types of nonparametric and robust regression, these methods are less standardized; different software packages implement different methods, and a method with a given name may be implemented differently in different packages. Specialized regression software has been developed for use in fields such as survey analysis and neuroimaging.

---