Adaboost (Adaptive Boosting) in Python

Boosted trees attract attention of data people. Even though gradient boosting methods are more popular, adaboost is a strong technique. It is mainly based on the principle that weak classifiers come together and create a powerful one. A weak classifier might be a basic decision stump or single layer perceptron. You might think that an individual employee cannot move a heavy rock but poor employees come together and move a heavy rock.

Herein, chefboost is a python based gradient boosting, random forest and adaboost enabled decision tree framework. In this video, we are going to see how to apply adaboost for a binary classification problem in Python. You need to write just a few lines of code.

Framework: https://github.com/serengil/chefboost

Tutorial: https://sefiks.com/2018/11/02/a-step-by-step-adaboost-example/

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