Ensemble Learning techniques combine multiple machine learning models to improve performance, accuracy, and stability.
Popular types of Ensemble Learning methods include:
Bagging: Bootstrap Aggregating, where multiple models are trained on randomly sampled subsets of the training data to reduce overfitting and increase stability.
Boosting: Ensemble method that sequentially trains models, with each subsequent model correcting the errors of the previous model to improve accuracy.
Stacking: Combines predictions from multiple models, using a meta-model to learn how to weigh best and integrate the predictions to improve accuracy.
Cascade: A hierarchical ensemble method that combines multiple classifiers sequentially, with each subsequent model used to correct errors from the previous model.
Bayesian Model Combination: Bayesian-based method that combines multiple models by weighting them according to their probability of being correct.
These techniques enable better performance and accuracy than individual models by leveraging the strengths of individual models while mitigating their weaknesses