Stacking models in Python efficiently
Ensembles have rapidly become one of the hottest and most popular methods in applied machine learning. Virtually every winning Kaggle solution features them, and many data science pipelines have ensembles in them.
Put simply, ensembles combine predictions from different models to generate a final prediction, and the more models we include the better it performs. Better still, because ensembles combine baseline predictions, they perform at least as well…
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