Ensemble methods
Ensembling models are a robust approach to enhancing the efficiency of the predictive models. It is a well-thought out strategy that is very similar to a power-packed word—TEAM !! Any task done by a team leads to significant accomplishments.
What are ensemble models?
Likewise, in the ML world, an ensemble model is a team of models operating together to enhance the result of their work. Technically, ensemble models comprise of several supervised learning models that are individually trained, and the results are merged in various ways to achieve the final prediction. This result has higher predictive power than the results of any of its constituting learning algorithms independently.
Mostly, there are three kinds of ensemble learning methods that are used:
- Bagging
- Boosting
- Stacking/Blending
Bagging
Bagging is also known as bootstrap aggregation. It is a way to decrease the variance error of a model's result. Sometimes the weak learning algorithms are very sensitive—a slightly different...
