Ensemble averaging is a technique used in machine learning and statistics where multiple models, or 'ensemble members', are trained and their predictions are combined to produce a more robust and accurate output. This approach reduces the risk of overfitting to any single model by averaging the results, thus providing a better generalization to unseen data. It is commonly utilized in various ensemble methods, including random forests, where individual decision trees contribute to the final prediction.
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