Ensemble methods combine multiple models for better performance from "summary" of Machine Learning by Ethem Alpaydin
Ensemble methods are a powerful approach in machine learning where multiple models are combined to achieve better performance than any individual model. The idea behind ensemble methods is that by combining the predictions of multiple models, we can reduce the variance and bias of the overall model, leading to more accurate and robust predictions. There are several different ways to combine models in ensemble methods. One common approach is to train multiple models on different subsets of the data, such as using different features or different samples of the training data. These models are then combined to make predictions, often by taking a weighted average of their individual predictions. Another approach to ensemble methods is to train multiple models using the same data but with different algorithms or hy...
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