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Introduction to boosted decision trees

Introduction to boosted decision treesKatherine WoodruffMachine Learning Group MeetingSeptember to BDTs decision trees boosting Gradient and how to use them Common hyperparameters Pros and tutorial Uses xgboost library (python API) See next slide2 Before we are three options for following the notebook from github and run it You need Jupyter notebook, numpy, matplotlib, pandas installed git clone The data used in the tutorial is included in the repository (only ~2MB) Then just install xgboost (instructions are also in the notebook) the code from the notebook If you don t have Jupyter, but do have numpy, matplotlib, and pandas Can install xgboost and copy the code directly from the notebook and execute it in an ipython session Can download the data here: observe If you don t have and don t want to install the python packages You can follow along by eye from the link in option 23 The hands-on tutorial is in Jupyter notebook form and uses the XGBoost python you want to do 1 or 2 you should start the xgboost installation

Gradient boosting 2. When and how to use them Common hyperparameters Pros and cons 3. Hands-on tutorial Uses xgboost library (python API) See next slide 2.

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