Xgboost For Regression Predictive Modeling And Time Series Analysis Build Intuitive Understanding Develop Build Evaluate And Deploy Model Partha Pritam Deka by Partha Pritam Deka, Joyce Weiner 9781805123057, 180512305X instant download after payment.

As you progress, you'll get to grips with the XGBoost algorithm, including its mathematical underpinnings and its advantages over other ensemble methods. You'll learn when to choose XGBoost over other predictive modeling techniques, and get hands-on guidance on implementing XGBoost using both the Python API and scikit-learn API. You'll also get to grips with essential techniques for time series data, including feature engineering, handling lag features, encoding techniques, and evaluating model performance. A unique aspect of this book is the chapter on model interpretability, where you'll use tools such as SHAP, LIME, ELI5, and Partial Dependence Plots (PDP) to understand your XGBoost models. Throughout the book, you'll work through several hands-on exercises and real-world datasets.

By the end of this book, you'll not only be building accurate models but will also be able to deploy and maintain them effectively, ensuring your solutions deliver real-world impact.