treebased

Treebased describes models that build decision trees to partition data into regions with similar outcomes. A tree has nodes for feature tests and leaves with predictions; at each internal node, a feature is tested and data split accordingly, propagating to further nodes. These methods are used for supervised learning on structured data and can model nonlinear relationships and feature interactions without assuming a fixed parametric form.

Key varieties include decision trees (often implemented as CART), random forests, and gradient-boosted trees. Random forests combine many decorrelated trees trained on bootstrap samples with aggregation to improve accuracy and stability. Gradient boosting builds trees sequentially, where each new tree aims to correct the errors of the previous ensemble, with popular implementations such as XGBoost and LightGBM. There are also Extremely Randomized Trees (extra trees).

Strengths of treebased models include flexibility with minimal preprocessing, ability to handle mixed feature types, and interpretability of individual trees alongside global measures of feature importance in ensembles. They perform well on tabular data, accommodate missing values in various ways, and can model complex nonlinear patterns and interactions. Ensemble methods often yield strong predictive performance and robustness to overfitting when properly tuned.

Limitations include a tendency of single trees to overfit, especially with noisy data. Ensembles improve accuracy but reduce interpretability and can require substantial training time and memory on large datasets. Performance can be sensitive to hyperparameters such as tree depth, learning rate, number of estimators, and subsampling strategies.

Applications of treebased methods span classification and regression on financial, medical, and industrial data, as well as feature importance analysis, anomaly detection, and ranking. They remain a default choice for many practitioners when working with tabular datasets due to their versatility and strong empirical performance.