Ensemble Methods
Ensemble methods are a set of techniques in machine learning and data science that combine multiple models to improve the overall performance of the system. The idea behind ensemble methods is to leverage the strengths of different models and reduce their weaknesses by combining them. Ensemble methods can be used for both classification and regression problems. There are two main types of ensemble methods: bagging and boosting. Bagging involves training multiple models independently on different subsets of the training data and then combining their predictions. Boosting, on the other hand, involves training models sequentially, with each subsequent model trying to correct the errors of the previous ones. Ensemble methods have been shown to be effective in improving the accuracy and robustness of machine learning models, especially in cases where individual models may overfit or underfit the data.
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