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Model Ensemble
Model ensembling combines the predictions of multiple individual machine learning models to produce a single, more accurate prediction. Instead of relying on one 'expert,' it leverages the collective intelligence of several models, each potentially good at different aspects of the problem. Common methods include bagging, boosting, and stacking.
In plain terms
It's like getting opinions from several expert consultants on a complex problem rather than just one.
Why it matters
It typically leads to more stable and accurate predictions than any single model alone, reducing the risk of errors.
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