Hands–On Gradient Boosting with XGBoost and scikit–learn Perform accessible machine learning and extreme gradient boosting

The book starts with an introduction to machine learning and XGBoost before gradually moving on to gradient boosting. You'll cover decision trees in detail and analyze bagging in the machine learning context. You'll then learn how to build gradient boosting models from scratch and extend gradient boosting to big data to recognize their limitations. The book also shows you how to implement fast and accurate machine learning models using XGBoost and scikit-learn and takes you through advanced XGBoost techniques by focusing on speed enhancements, deriving parameters mathematically, and building robust models. With the help of detailed case studies, you'll practice building and fine-tuning regressors and classifiers and become familiar with new tools such as feature importance and the confusion matrix. Finally, you'll explore alternative base learners, learn invaluable Kaggle tricks such as building non-correlated ensembles and stacking, and prepare XGBoost models for industry deployment with unique transformers and pipelines.