Applied Classification with XGBoost 1
Using Jupyter notebook demos, you'll experience preliminary exploratory data analysis. You will create a classification model with XGBoost. Using third-party libraries, you will explore feature interactions, and explaining the models.
What you'll learn
Are you a data professional who needs a complete, end-to-end classification demonstration of XGBoost and the libraries surrounding it? In this course, Applied Classification with XGBoost, you'll get introduced to the popular XGBoost library, an advanced ML tool for classification and regression. First, you'll explore the underpinnings of the XGBoost algorithm, see a base-line model, and review the decision tree. Next, you'll discover how boosting works using Jupyter Notebook demos, as well as see preliminary exploratory data analysis in action. Finally, you'll learn how to create, evaluate, and explain data using third party libraries. You won't be using the Iris or Titanic data-set, you'll use real survey data! By the end of this course, you'll be able to take raw data, prepare it, model a classifier, and explore the performance of it. Using the provided notebook, you can follow along on your own machine, or take and adapt the code to your needs.
Table of contents
About the author
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. He blogs at ``hairysun.com`` and occasionally tweets useful Python related information at ``@__mharrison_... more