Ensembling and Stacking

Ensembling and Stacking

Ensembling and Stacking. Stacked Generalization or “Stacking” for short is an ensemble machine learning algorithm.

Ensemble to harness the true potential of all models combined!!

You must’ve come across these terms in Kaggle competitions before. Nowadays, Ensembling and *Stacking *of small models is even done at industrial levels due to advancements in computing power. Moreover, smaller models are easier to fine-tune.

Ensembling:

Ensembling is nothing but a combination of different models. The models can be combined by their predictions/probabilities. One way would be to compute an _average _of all the predictions from the models ( Note: If outliers are present, _median _would perform better).

Ensemble Probabilities = (M1 + M2 + … + Mn) / n

where M(i) = probability of ith model

*A Random Forest is an ensemble of multiple decision trees. *However, as shown in the above diagram, combination of different models like KNN, SVM, Logistic regression can also be ensembled.

kaggle bagging ensemble-learning stacking machine-learning

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