Ensemble Learning is a machine learning technique that involves combining the predictions of multiple individual models (learners) to improve overall performance and robustness. The idea is that by combining diverse models, the strengths of one model can compensate for the weaknesses of another, resulting in a more accurate and reliable predictive model. Ensemble methods can be applied to various types of machine learning algorithms, including decision trees, neural networks, and support vector machines. There are different ensemble learning approaches, with two of the most common being bagging and boosting:

Bagging (Bootstrap Aggregating):

Bagging involves training multiple instances of the same learning algorithm on different subsets of the training data, created by random sampling with replacement (bootstrap sampling). The final prediction is often obtained by averaging (for regression) or voting (for classification) over the predictions of individual models.

Boosting:

Boosting aims to improve the accuracy of a weak learner by sequentially training multiple models, each giving more emphasis to the instances that the previous models misclassified. The final prediction is typically a weighted combination of the predictions from all models. Popular boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.

Random Forest:

Random Forest is a specific ensemble learning method that builds a collection of decision trees. Each tree is trained on a random subset of the features and, for each split, considers a random subset of the features. The final prediction is made by aggregating the predictions of all the trees.

Stacking: