| Linear Regression | Simple linear trends | Easy to interpret, computationally efficient | Assumes a linear relationship, sensitive to outliers |
| Polynomial Regression | Non-linear patterns | Captures curvilinear trends, flexible | Prone to overfitting, requires careful tuning of polynomial degree |
| Ridge Regression | Multicollinearity (correlated features) | Reduces overfitting, stabilizes coefficients | Doesn’t perform feature selection |
| Lasso Regression | Feature selection (reducing unnecessary predictors) | Performs feature selection by shrinking coefficients to zero | May remove useful variables |
| ElasticNet Regression | High-dimensional data with correlated features | Balances Ridge and Lasso, good for complex models | Requires tuning of two hyperparameters (L1 and L2 regularization) |
| Logistic Regression | Binary classification (0/1) | Easy to interpret, probabilistic output | Assumes linear decision boundary, may not work well with non-linear relationships |
| Multivariate Regression | Predicting multiple dependent variables | Handles multiple outcomes, useful for interdependent factors | Complex interpretation, requires more data |
| Support Vector Regression (SVR) | Complex, non-linear relationships | Handles non-linearity, robust to outliers | Computationally expensive, requires careful kernel selection |
| Decision Tree Regression | Simple, rule-based predictions | Easy to interpret, works well with mixed data types | Prone to overfitting, sensitive to outliers |
| Random Forest Regression | High accuracy, generalization | Handles large datasets, reduces overfitting | Computationally expensive, less interpretable |