Ensembling

Ensembling is a technique in machine learning where multiple models are combined to improve the overall performance of the system. The idea behind ensembling is that by combining several models, the strengths of each model can be leveraged while minimizing their weaknesses. Ensembling can be done in various ways, such as by averaging the predictions of multiple models, using a weighted average, or using more complex methods such as stacking or boosting. Ensembling is particularly useful when dealing with complex datasets or when individual models are prone to overfitting. It can also be used to improve the robustness of a system by reducing the impact of outliers or errors in individual models.
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