Build an understanding of XGBoost and gain hands-on experience with the XGBoost Python API through multiple practical use cases for classification, Regression and Time series analysis including model testing and deployment.Key Features
- Quick start guide using XGBoost to build a classifier, getting you up and running right away
- Easy-to-follow deep dive explanation of the XGBoost technical paper
- Application of XGBoost to time series data covering moving average, frequency, and window methods
Book Description
XGBoost is a popular open-source library that provides an efficient, effective, scalable and high-performance implementation of the gradient boosting algorithm. You will be able to build an intuitive and practical understanding of the XGBoost algorithm through our demystifying the complex math underneath and explanation of XGBoost’s benefits over other decision tree ensemble models, including when to use XGBoost or other prediction algorithms. This book provides a hands-on approach to implementation of the XGBoost Python API as well as the scikit-learn API that will help one to be up-and-running and productive in no time. Complete with step-by-step explanations of essential concepts, as well as practical examples, this book begins with a brief introduction to machine learning concepts, then dives into the fundamentals of XGBoost and its benefits before exploring practical applications. You will get hands-on experience using XGBoost through practical use cases on classification, regression, and time-series data. By the end of this book, you will have an understanding of the XGBoost algorithm, have installed the XGBoost API, downloaded and prepared a practical dataset, trained the XGBoost model, make predictions, and evaluated and deployed models using the Python and scikit-learn API.
What you will learn
- Build a strong intuitive understanding of the XGBoost algorithm and its benefits
- Gain hands-on experience with the XGBoost Python API through multiple practical use cases for classification, Regression and Time series analysis
- Get experience with feature engineering, feature selection and categorical encoding
- Evaluate models using various metrics
- Gain hands-on experience with XGBoost model deployment
Who this book is for
This book is for data scientists, machine learning developers, and anyone with basic coding knowledge and familiarity with Python, GitHub and other Dev Ops tools, looking to build effective predictive models using XGBoost. We address the top three common problems when building predictive models: problems with available data such as missing data and non-normal data, the desire to combine numeric and text (categorical) data, how to get value out from non-numeric data to improve predictions, and how to deploy and sustain a model, how to measure and improve model fitting.
