Ensemble learning improves the performance of machine learning models by combining multiple models to produce better results than any individual model. It works on the principle of "wisdom of the crowd," where diverse models are trained on the same data and their predictions are aggregated to make a final prediction. This approach reduces overfitting and improves generalization, as different models may capture different aspects of the underlying data patterns.

Ensemble learning includes techniques like bagging, boosting, and stacking. Bagging (Bootstrap Aggregating) builds multiple models independently and then combines them by averaging the predictions, reducing variance. Boosting builds models sequentially, with each new model correcting errors made by previous ones, reducing bias. Stacking combines multiple models using another model to learn how to best combine the base models' predictions.

Overall, ensemble learning is a powerful technique in data science and machine learning, often leading to more accurate and robust models. Understanding ensemble learning is crucial for anyone pursuing a data science and machine learning course, as it provides a key strategy for improving model performance.

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