Gradient Boosting
Gradient Boosting is an ensemble learning technique that builds a model as a sequence of weak learners, typically shallow decision trees, where each new learner is trained to correct the residual errors of the combined ensemble so far.
6 resources across 2 libraries
Glossary Terms(5)
Ensemble Learning
Ensemble learning is a machine learning approach that combines the predictions of multiple individual models to produce a single, typically more accurate and r…
Random Forest
Random Forest is an ensemble learning algorithm that builds many decision trees on randomly resampled data and random feature subsets, then aggregates their pr…
Gradient Boosting
Gradient Boosting is an ensemble learning technique that builds a model as a sequence of weak learners, typically shallow decision trees, where each new learne…
XGBoost
XGBoost (Extreme Gradient Boosting) is an open-source, highly optimized implementation of the gradient boosting algorithm, widely used for structured/tabular d…
Decision Tree
A decision tree is a supervised learning model that predicts an outcome by learning a hierarchy of simple, interpretable if-then decision rules based on input…