Ensemble learning is a machine learning concept that involves the combination of several base models in order to produce one optimal predictive model. The main idea behind ensemble learning is that when you combine various models, the resultant model will perform better than any individual model due to its better prediction and generalization.
How Ensemble Learning works
Ensemble methods work by conducting a series of base learners and combining their outputs according to certain rules. There are different ways to form ensemble models, the most common ones being bagging, boosting, and stacking.
- Bagging (Bootstrap Aggregating): This method works by generating multiple subsets from the original data, training a model on each, and then integrating their predictions. This reduces variance and helps to avoid overfitting. A common example of bagging is Random Forests.
- Boosting: In boosting, models are trained sequentially with each model learning from the mistakes of its predecessors. It starts by fitting an initial model to the data then fitting additional models to the residuals. The final prediction is a weighted sum of the predictions made by previous models. Examples include AdaBoost and Gradient Boosting.
- Stacking: This method combines several different models (can be different or the same algorithms) and uses a meta-model which summarizes the predictions of the individual models. It is typically used for improving prediction accuracy.
Ensemble learning is not always the best solution due to increased complexity and computation time. However, when model performance is the top priority and computational resources are available, ensemble methods often provide superior results.
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