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Machine Learning Algorithm: Linear Regression
Background
The origins of linear regression can be traced back to the 18th century, when mathematician and astronomer Pierre-Simon Laplace used regression analysis to study the relationship between astronomical observations and celestial motion. However, it was not until the 19th century that regression analysis was formalized and developed as a statistical method.
One of the earliest and most influential contributions to the development of linear regression was made by the English statistician Francis Galton, who used regression analysis to study the relationship between height and other physical characteristics in families. Galton’s work laid the foundation for the use of regression analysis in genetics and biology.
In the early 20th century, the Norwegian statistician Carl Friedrich Gauss and the Italian statistician Alfonso Catoni made important contributions to the development of regression analysis, including the formulation of the method of least squares, which is still widely used in regression analysis today.
In the 1920s and 1930s, the development of linear regression was further advanced by the work of the British statistician Ronald A. Fisher, who made important contributions to the design of experiments, hypothesis testing, and the use of regression analysis in the social sciences.
Today, linear regression is widely used in many fields, including economics, finance, engineering, biology, and psychology, and is considered a fundamental tool in predictive modeling and data analysis.
The Basics
Linear Regression is a simple yet powerful machine learning algorithm used for predictive modeling.
It is used technique for continuous target variables
It is a supervised learning algorithm that tries to fit a linear equation to the relationship between a dependent variable (target) and one or more independent variables (predictors).
The main objective of linear regression is to minimize the difference between the observed target values and the predicted target values.
The basic equation for a simple linear regression model with a single predictor variable x is:
