Ensemble learning is a computational technique in which classifiers or experts are strategically formed and combined. It is used to improve classification, prediction, and function approximation of any model.

Ensemble learning is a machine learning technique that involves combining multiple individual models (often called “base learners” or “weak learners”) to create a stronger, more accurate model. The idea behind ensemble learning is to leverage the diversity of the individual models to improve overall prediction performance.

There are several approaches to ensemble learning, including:

Bagging (Bootstrap Aggregating): In bagging, multiple instances of the same base learning algorithm are trained on different subsets of the training data, usually sampled with replacement. The final prediction is often the average (for regression) or majority vote (for classification) of the predictions of all the individual models.
Boosting: Boosting works by sequentially training models where each subsequent model focuses on the mistakes made by the previous ones. Popular boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.
Random Forest: Random Forest is an ensemble learning method that combines bagging with feature randomization. It trains a collection of decision trees where each tree is trained on a random subset of the features.
Stacking: Stacking involves training a meta-learner (or “stacking model”) that takes predictions from multiple base learners as input and combines them to make a final prediction.

Ensemble learning can often outperform individual models by reducing overfitting, increasing robustness, and capturing more complex relationships in the data. It is widely used in various machine learning tasks, including classification, regression, and anomaly detection.