AI GlossaryEnsemble LearningENTRY — Machine Learning
[Machine Learning]
Ensemble Learning.
Combining multiple models to produce better predictions than any single model.
In-depth explanation
01Ensemble methods leverage the wisdom of crowds—combining diverse models often outperforms individual models. Main approaches include bagging (training models on bootstrap samples, like Random Forest), boosting (sequentially training models to correct predecessors' errors, like XGBoost), and stacking (using a meta-model to combine predictions).
Examples
02EX. 01
Random Forest
EX. 02
XGBoost
EX. 03
Model stacking in competitions
More in Machine Learning
0301ClassificationPredicting which category or class an input belongs to from a set of predefined categories.02Cross-ValidationA technique to evaluate model performance by training and testing on different subsets of data.03FeatureAn individual measurable property or characteristic of data used as input to a machine learning model.04Feature EngineeringThe process of using domain knowledge to create new features that improve model performance.05Gradient DescentAn optimization algorithm that iteratively adjusts model parameters to minimize the loss function.06HyperparameterConfiguration settings set before training that control the learning process, not learned from data.
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