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
SHARE
Related Links
Traditional email campaigns, though cost-effective, often fall short due to their lack of personalization, leading to…
Customer Lifetime Value (CLV) is no longer just a metric—it’s a strategic asset that can shape…
