Linear Regression: A Cornerstone of Machine Learning
1.Understanding Linear Regression Linear regression is a fundamental statistical method and a core algorithm in machine learning. It's used to model the relationship between a dependent variable (the target) and one or more independent variables (the features). The goal is to find the best-fitting line that minimizes the distance between the predicted values and the actual values. The Mathematical Model Mathematically, a simple linear regression model can be represented as: y = mx + b where: * `y`: The dependent variable * `x`: The independent variable * `m`: The slope of the line * `b`: The y-intercept In the context of machine learning, we often deal with multiple independent variables. This is known as multiple linear regression, and the equation becomes: y = b0 + b1x1 + b2x2 + ... + bnxn where: * `y`: The dependent variable * `x1, x2, ..., xn`: The independent variables * `b0, b1, b2, ..., bn`: The coefficients The Learning Process The key to linear ...