Linear Regression in Machine Learning is a fundamental statistical method within supervised learning used to model the linear relationship between one or more independent variables and a continuous dependent variable. The core principle relies on minimizing the sum of squared residuals through methods such as Ordinary Least Squares (OLS) or Maximum Likelihood Estimation, adhering to assumptions including linearity, independence of errors, homoscedasticity, and normal distribution of error terms. This concept serves as the foundational optimization framework for understanding gradient descent mechanisms that underpin more complex deep learning architectures in artificial intelligence research.
Ordinary Least Squares (OLS) linear regression is a statistical mechanism for modeling the relationship between a dependent variable and one or more independent variables by estimating unknown coeffi…