Abstract: This tutorial will show how to use XGBoost. It will demonstrate model creation, model tuning, model evaluation, and model interpretation.
The XGBoost library is one of the most popular libraries with data scientists for creating predictive models with structured (or tabular) data. This tutorial will cover the library, tuning it, evaluating models created by it, and understanding predictions from it. Attendees will have the chance to try it out with the labs.
60 % lecture
40 % lab
* Installation and Jupyter - 10 min
* Creating Models - 30 min
* Lab - 20 min
* Model Evaluation - 15 min
* Model Tuning - 15 min
* Lab - 30 min
* Model Interpretation - 20 min
* Lab - 20 min
The audience for End to end machine learning with XGBoost is someone familiar with Python and curious about XGBoost. Generally theses are data scientists, data analysts, or engineers, but curiosity is sufficient. Attendees should have a basic command of the Python language, creating classes, functions, and installing libraries. The user will learn how to use the XGBoost and ancillary libraries.
Bio: Matt Harrison has been using Python since 2000. He runs MetaSnake, a Python and Data Science consultancy and corporate training shop. In the past, he has worked across the domains of search, build management and testing, business intelligence, and storage.
He has presented and taught tutorials at conferences such as Strata, SciPy, SCALE, PyCON, and OSCON as well as local user conferences.