Ensemble Learning is a powerful technique used in machine learning where multiple models (such as decision trees, classifiers, or regressors) are combined to improve the overall performance and predictive accuracy of the system. The basic idea behind Ensemble Learning is that by combining the predictions of multiple models, we can often achieve better results than any single model would on its own. There are different types of Ensemble Learning methods, such as Bagging (Bootstrap Aggregating), Boosting, and Stacking. In Bagging, multiple models are trained on different subsets of the data, and their predictions are aggregated to make the final prediction. Boosting, on the other hand, works by training models sequentially, where each subsequent model corrects the errors of its predecessor. Stacking involves training multiple models and then using another model to learn how to best combine their predictions. One of the most famous examples of Ensemble Learning is the Random Forest algorithm, which combines multiple decision trees to make predictions. Each decision tree is trained on a random subset of the data, and the final prediction is made by averaging the predictions of all the trees. Ensemble Learning is widely used in various real-world applications such as in recommendation systems, fraud detection, and medical diagnosis. It is particularly useful when dealing with complex and noisy data, as it can help improve generalization and reduce overfitting. By leveraging the diversity of multiple models, Ensemble Learning can often outperform individual models and produce more reliable results.

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What is Ensemble Learning?

Saurabh Prajapati

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Ensemble Learning

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