Ensembling

Definition of Ensembling

Ensembling: Ensembling is a technique used in machine learning that consists of combining the predictions of multiple models in order to improve the accuracy of the predictions.

What is Ensembling used for?

Ensembling is a technique in data science and machine learning in which multiple prediction models are combined to produce a more accurate predictive result. It can be used to improve the accuracy of a single model or to create a stronger predictive result when combining weak models. Ensembling works by training different base models on the same training dataset and combining their predictions either through majority voting or weighted averaging. Majority voting involves taking the mode of all predictions, while weighted averaging takes account of performance metrics such as accuracy or log loss.

Ensembling is used for many tasks in data science and machine learning, such as classification, regression, clustering, recommendation systems, and forecasting. By combining multiple predictive models into one model, ensembling can reduce variance errors that are caused by overfitting or underfitting on certain datasets. Furthermore, ensembling can help improve model accuracy by creating more diverse predictions that take account of different perspectives from different base models. In addition to improving the accuracy of prediction results, ensembling also helps make predicted results more robust since it reduces outliers from individual base model outputs.