Freaks City

Linear Regression Fundamentals The linear model is defined as: $y = Xw + b + \epsilon$, where $w$ represents weights and $b$ is the bias term. Model evaluation relies on loss functions that quantify prediction errors: MSE Loss Function: $$ l^{(i)}(w,b) = \frac{1}{2}(\hat{y}^{(i)} - y^{(i)})^2 \ L(w,b) = \frac{1}{n}\sum_{i=1}^{n}l^{(i)}(w,b) $$ ...