Linear Regression is a foundational statistical method and supervised learning algorithm used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.
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Linear regression is a foundational supervised learning algorithm used to model the relationship between a dependent target variable and one or more independent predictor features. It assumes a linear relationship, fitting a straight line (or hyperplane in multiple dimensions) that minimizes the sum of squared differences between predicted values and actual data points.
y = mx + b where y is the target, x is the feature, m is the slope (weight), and b is the intercept (bias).
OLS is a method for estimating the parameters in linear regression by minimizing the sum of squared residuals (errors) between predicted and actual values.
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