Day 51 of #60daysOfMachineLearning
π· Ensemble Learning π·
Ensemble learning is a machine learning technique that combines multiple models to improve the performance and robustness of the final model.
π· Ensemble Learning π·
Ensemble learning is a machine learning technique that combines multiple models to improve the performance and robustness of the final model.
In ensemble learning, the individual models, called base models or weak learners, are trained on the same data, and their predictions are combined using a combination rule, such as majority voting, averaging, or weighted averaging.
Ensemble learning can be used for a variety of tasks, such as classification, regression, and clustering. Some common ensemble learning algorithms include bagging, boosting, and stacking.
Ensemble learning can provide better performance and more robust predictions than individual models, and it can be used to reduce overfitting and improve the generalizability of the final model.
However, ensemble learning can be computationally expensive, and it can require careful design and tuning of the base models and the combination rule.
Overall, ensemble learning is a powerful and useful technique in machine learning that can help improve the performance and reliability of predictive models.
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