Linear Models
Introduction In a linear model, the goal is to find the best-fit line or hyperplane that minimizes the difference between the predicted output and the actual output. The predicted output is obtained by multiplying the input features with their corresponding weights (coefficients) and adding a bias term. Mathematically, the linear model can be represented as: y = w₁x₁ + w₂x₂ + ... + wₙxₙ + b Where: y is the predicted output or target variable. x₁, x₂, ..., xₙ are the input features. w₁, w₂, ..., wₙ are the weights (coefficients) associated with the input features. b is the bias term or intercept. The linear model aims to learn the optimal values for the weights (w₁, w₂, ..., wₙ) and the bias term (b) from the training data. This process is typically done by minimizing a cost function, such as mean squared error (MSE) or mean absolute error (MAE), using techniques like ordinary least squares (OLS) or gradient descent. Linear models have several advantages, including simplicity, interpret...