Linear Models Regression, Classification, Loss Functions, and Regression Models

Описание: I discussed the fundamentals of linear models, which predict a target Y as a linear combination of input features. I explained that linear models are used for regression and classification, and are loss functions that measure how wrong the model's predictions are. I also covered common loss functions such as squared error, logistic loss, and hinge loss. Furthermore, I introduced key regression models including ordinary least squares, ridge regression, lasso regression, and elastic net, which use different penalties to shrink or zero out coefficients and features.