Instructions If there is a conflict bewteen the problem description in the ipython notebook and the question in the pdf, follow the question in the pdf file. The part you need to fill in is commented as “Code Clip”. You can search “Code Clip” in this notebook to find the part you need to complete. After you finish the required part, you may need to run other related code blocks for evaluation or visualization. »
Boosting a decision stump The goal of this notebook is to implement your own boosting module.
Go through an implementation of decision trees. Implement Adaboost ensembling. Use your implementation of Adaboost to train a boosted decision stump ensemble. Evaluate the effect of boosting (adding more decision stumps) on performance of the model. Explore the robustness of Adaboost to overfitting. This file is adapted from course material by Carlos Guestrin and Emily Fox. »