◐ learning · kind algorithm · level 2 · 14h
- Generaliza: Decision Trees
- Requiere: Gradient Descent
Fits trees sequentially, each correcting the residual errors of the last. XGBoost/LightGBM dominate tabular competitions.
Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals instead of residuals as in traditional boosting. It gives a prediction model in the form of an ensemble of weak prediction models, i.e., models that make very few assumptions about the data, which are typically simple Decision Trees. When a decision tree is the weak learner, the resulting algorithm is called gradient-boosted trees; it usually outperforms Random Forest.
Enlaces
- Generaliza: Decision Trees
- Requiere: Gradient Descent