Ensemble Learning
Ensemble Learning is a machine learning technique that combines multiple models to improve the overall performance of the system. It involves training multiple models on the same dataset and then combining their predictions to make a final prediction. Ensemble Learning can be used for both classification and regression problems. There are different types of Ensemble Learning techniques such as Bagging, Boosting, and Stacking. Bagging involves training multiple models independently on different subsets of the data and then combining their predictions. Boosting involves training models sequentially, where each subsequent model tries to correct the errors of the previous model. Stacking involves training multiple models and then using another model to learn how to best combine their predictions. Ensemble Learning is often used in situations where a single model may not be able to capture the complexity of the data or when the dataset is noisy or imbalanced.
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