Ensemble Methods

AdaBoost is a machine learning algorithm that improves model accuracy by combining multiple weak classifiers into a strong one.

Bagging is a machine learning ensemble technique that improves accuracy by combining multiple models.

Boosting is a machine learning technique that improves model accuracy by combining weak learners into a strong learner.

Bootstrap aggregating, or bagging, is a machine learning ensemble technique that improves model accuracy by combining multiple models.

A committee machine is an ensemble learning model that combines multiple neural networks for improved performance.

Functional Gradient Boosting is a machine learning technique that builds models in a stage-wise manner to improve prediction accuracy.

Gradient Boosting is a machine learning technique that builds models sequentially to improve prediction accuracy.