Machine Learning Fundamentals · Supervised learning

Ensemble methods

Combining many weak models (usually trees) into one stronger one, either by averaging (bagging) or correcting each other's mistakes in sequence (boosting).

Also known as: random forest, gradient boosting, bagging, boosting.

Start a lesson on Ensemble methods

Learn first

• Decision trees: A model that predicts by asking a sequence of yes/no questions about the input, like a flowchart.

Related topics

• Hyperparameter tuning: Systematically searching for the settings (learning rate, regularization strength, tree depth) that aren't learned from data but chosen before training.