regression

**Regression Analysis** **Overview** Regression is a type of Supervised Learning where the goal is to predict a **continuous** numerical value (Temperature, Price, Age), as opposed to a categorical Class (Dog/Cat). **Types** **1. Linear Regression** Fitting a straight line ($y = mx + b$) to data. - **Metric**: R-Squared ($R^2$), Mean Squared Error (MSE). - **Assumptions**: Linear relationship, homoscedasticity (constant variance). **2. Polynomial Regression** Fitting a curve ($y = ax^2 + bx + c$). - **Risk**: Overfitting (wiggling too much to hit every point). **3. Ridge / Lasso Regression** Linear regression with **Regularization** to prevent overfitting. - **L1 (Lasso)**: Shrinks weights to 0 (Feature Selection). - **L2 (Ridge)**: Shrinks weights towards 0 (Stability). **Evaluation** - **MAE (Mean Absolute Error)**: "On average, I am off by $5k." (Robust to outliers). - **RMSE (Root Mean Squared Error)**: "I am off by $5k, but errors are squared." (Penalizes huge errors heavily). "Regression identifies the relationship between a dependent variable and one or more independent variables."