What does ensemble learning aim to achieve?

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Ensemble learning aims to combine multiple models to improve the accuracy and performance of predictions. This technique leverages the unique strengths of various models, allowing them to complement each other and mitigate weaknesses inherent in individual models. By aggregating the predictions from different models, whether through methods like bagging, boosting, or stacking, ensemble learning often achieves better generalization than any single model could.

The approach helps in reducing the likelihood of overfitting and enhances robustness to fluctuations in data. Consequently, this improved predictive performance is particularly valuable in complex datasets where diverse perspectives from different learning algorithms can lead to insights that enhance overall predictive power.

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