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Ensemble Learning is a machine learning approach that involves training multiple models on different subsets of data, and then combining their predictions to produce a final output. This technique is also known as committee-based learning or multi-model learning.

The basic idea behind ensemble learning is to leverage the strengths of individual models by combining their predictions. Each model in the ensemble is trained on a different subset of the data, which can include different features, different classes, or different subsets of the same features. By combining the predictions of these individual models, the ensemble can make more accurate predictions than any one of the individual models.

Ensemble learning can be used for various machine learning tasks, including classification, regression, and clustering. Some common ensemble techniques include bagging, boosting, and stacking. Bagging involves training multiple models on different subsets of the data and then combining their predictions using voting or averaging. Boosting involves training a sequence of models, where each subsequent model is trained to correct the errors of the previous model. Stacking involves training a meta-model to combine the predictions of multiple base models.

Ensemble learning has several benefits, including improved accuracy, increased robustness, and reduced overfitting. However, it can also increase the computational cost and complexity of the learning process. The choice of ensemble technique depends on the specific problem and data, as well as the strengths and weaknesses of the individual models being combined.

Ensemble learning has been widely used in various domains, including image classification, natural language processing, and recommender systems. It is a powerful technique for improving the accuracy and robustness of machine learning models, and can be used in a variety of applications, including risk assessment, decision support systems, and predictive analytics.