Regression algorithms predict continuous outcomes based on input features. Key algorithms include:
Linear Regression: Models relationships with a linear equation.
Polynomial Regression: Uses polynomial equations for nonlinear relationships.
Ridge Regression: Adds regularization to linear regression to prevent overfitting.
Lasso Regression: Uses L1 regularization for feature selection.
Elastic Net Regression: Combines ridge and lasso penalties for correlated features.
Support Vector Regression (SVR): Applies SVM principles for regression tasks.
Decision Tree Regression: Uses tree structures to make continuous predictions.
Random Forest Regression: Combines multiple decision trees to enhance accuracy.
Gradient Boosting Regression: Sequentially builds models to correct previous errors.
XGBoost: An optimized gradient boosting algorithm for speed and performance.
Artificial Neural Networks (ANNs): Learns complex patterns through layered neurons.
Bayesian Regression: Uses Bayesian methods for probabilistic predictions.
Each algorithm has unique strengths, making them suitable for different types of regression tasks based on the dataset and problem requirements.