Least-Squares Function Fitting System

A Least-Squares Function Fitting System is a function fitting system that applies a least-squares algorithm to solve a least-squares task (with a least-squares loss function).

• Context:
  • It can range from being a Linear Least-Squares Regression System to being a Non-Linear Least-Squares Regression System.
  • It can range from being a Non-Regularized Least-Squares Function Fitting System to being a Regularized Least-Squares Function Fitting System.
• Example(s):
  • numpy.linalg.lstsq[1]
  • RLScore.
  • scipy.optimize.leastsq[2]
  • Least Squares-based Polynomial Function Fitting System, such as numpy.polyfit[3]
• Counter-Example(s):
  • Maximum Likelihood System,
• See: Newton's Method, Point Estimation System.

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