The following is a basic list of model types or relevant characteristics. There entires in these lists are arguable. For example: random forests theoretically use feature selection but effectively may not, support vector machines use L2 regularization etc.
- Bagging Models
- Bayesian Model
- Boosting Models
- Cost Sensitive Learning Models
- Discriminant Analysis Models
- Ensemble Model
- Feature Extraction Models
- Feature Selection Wrapper Models
- Gaussian Process Models
- Generalized Additive Model
- Generalized Linear Model
- Implicit Feature Selection Models
- Kernel Method
- L1 Regularization Models
- L2 Regularization Models
- Linear Classifier Models
- Linear Regression Models
- Logic Regression Models
- Logistic Regression Models
- Mixture Model
- Model Tree
- Multivariate Adaptive Regression Splines Models
- Neural Network Models
- Oblique Tree Models
- Partial Least Squares Models
- Polynomial Model
- Prototype Models
- Quantile Regression Models
- Radial Basis Function Models
- Random Forest Models
- Regularization Models
- Relevance Vector Machines
- Ridge Regression Models
- Robust Methods
- Robust Model
- ROC Curves Models
- Rule-Based Model
- Self-Organising Maps
- Support Vector Machines
- Tree-Based Model