Ensemble Learning

Instead of relying on a single model, ensemble methods leverage the strength of diverse models to make collective predictions. The key idea is that the combination of multiple models can often produce more robust and accurate results than any individual model.

There are several ensemble learning techniques, with the most common ones being:

• Bagging (Bootstrap Aggregating): it involves training multiple instances of the same model on different subsets of the training data, often obtained through bootstrapping (sampling with replacement). The final prediction is then averaged (for regression) or voted upon (for classification) across all models.
• Boosting: boosting focuses on sequentially training a series of weak learners (models that perform slightly better than random chance) and giving more weight to instances that were misclassified in the previous rounds. The final prediction is a weighted combination of the weak learners.
• [[Random forests]] a specific implementation of bagging using decision trees as the base models. Random Forests introduce additional randomness by considering only a subset of features at each split, enhancing diversity among the trees.
• Stacking: stacking involves training multiple diverse models and using another model (meta-model or blender) to combine their predictions. The idea is to let the meta-model learn the optimal way to combine the predictions of the base models.

Ensemble learning is widely used in various machine learning tasks, such as classification, regression, and even unsupervised learning. It helps mitigate overfitting, improve generalization, and enhance the overall performance of a machine learning system. Popular ensemble methods include [[Random forests]], Gradient Boosting Machines (e.g., XGBoost, [[AdaBoost]]), and ensemble neural networks.