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In machine learning (ML), Stacking is a method of ensemble learning where multiple individual models are combined to produce more accurate forecasts. The output of each model serves as input for a final ‘meta-model’, which learns how to weigh and combine the outcomes of all the individual models to get a final prediction.
Stacking
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