There are many types of regression models, each suited to different types of data and relationships. Some common types include:
1. Linear Regression: Models the relationship between two variables by fitting a straight line.
2. Logistic Regression: Used for binary classification, predicting probabilities for categories (e.g., yes/no, 0/1).
3. Polynomial Regression: Extends linear regression to model nonlinear relationships by using polynomial functions.
4. Ridge Regression: A type of linear regression that includes a regularization term to prevent overfitting.
5. Lasso Regression: Similar to ridge regression, but it can reduce some coefficients to zero, effectively selecting features.
6. Elastic Net Regression: Combines ridge and lasso regression for a balance between feature selection and regularization.
7. Quantile Regression: Estimates the median or other quantiles of the response variable, not just the mean.
8. Poisson Regression: Used for count data, modeling how often an event happens.
9. Ordinal Regression: Models ordinal (ranked) outcomes, where categories have an order but no specific distance between them.
10. Multinomial Logistic Regression: Extends logistic regression for multiclass classification problems.
11. Bayesian Regression: Applies Bayesian principles to linear regression for probabilistic prediction.
12. Support Vector Regression (SVR): A type of regression that uses support vector machine concepts for both linear and nonlinear relationships.
