How does ensemble learning improve model performance?

Ensemble learning is an effective technique for machine learning. It enhances the performance of models by combining several individual models into a single model that can make a more accurate prediction. Ensemble learning uses the strengths of multiple models to reduce the risk of error and increase overall reliability. This method is especially useful when dealing complex datasets, where individual models may struggle to capture all the underlying patterns. Data Science Classes in Pune

Ensemble learning is a powerful tool for improving performance. Individual models can suffer from high variance. This means that they may perform well with training data, but not so well with new data. Ensemble methods reduce the impact of errors in any one model by combining multiple models. Bootstrap Aggregating or Bagging is a widely-used technique that trains several models on subsets and aggregates the predictions. This reduces variance significantly.

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