Practical Guide to Boosting Algorithms In Machine Learning

Author(s): Youssef Hosni

Originally published on Towards AI.

Use weak learners to create a stronger one

Boosting (originally called hypothesis boosting) refers to any Ensemble method that can combine several weak learners into a strong learner. The general idea of most boosting methods is to train predictors sequentially, each trying to correct its predecessor. Firstly, a model is built from the training data. Then the second model is built to correct the errors present in the first model. This procedure is continued, and models are added until either the complete training data set is predicted correctly, or the maximum number of models is added. The final predictions are made by combining the predictions of all the… Read the full blog for free on Medium.

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Published via Towards AI