This section organizes common model families by the kind of structure they assume and the kinds of problems they are best suited for.
| Type | Best For | Examples |
|---|---|---|
| Linear models | Simple, interpretable tasks | Linear regression, logistic regression |
| Tree-based models | Tabular data, feature importance | Decision trees, random forests, XGBoost |
| Support vector machines | Classification with clear margins | SVM, SVR |
| Neural networks | Complex patterns, large data | MLPs, CNNs, RNNs |
| Transformers | Sequential and language data | BERT, GPT, T5 |
| Ensemble models | Combining multiple learners | Bagging, boosting, stacking |