XGBoost for Regression Predictive Modeling and Time Series Analysis Learn how to build

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XGBoost for Regression Predictive Modeling and Time Series Analysis: Learn how to build, evaluate, and deploy predictive models with expert guidance by Partha Pritam Deka, Joyce Weiner
English | December 13, 2024 | ISBN: 180512305X | 308 pages | PDF | 9.98 Mb
Master the art of predictive modeling with XGBoost and gain hands-on experience in building powerful regression, classification, and time series models using the XGBoost Python API

Key FeaturesGet up and running with this quick-start guide to building a classifier using XGBoostGet an easy-to-follow, in-depth explanation of the XGBoost technical paperLeverage XGBoost for time series forecasting by using moving average, frequency, and window methodsPurchase of the print or Kindle book includes a free PDF eBookBook Description
XGBoost offers a powerful solution for regression and time series analysis, enabling you to build accurate and efficient predictive models. In this book, the authors draw on their combined experience of 40+ years in the semiconductor industry to help you harness the full potential of XGBoost, from understanding its core concepts to implementing real-world applications.
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 Descriptions (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.
What you will learnBuild a strong, intuitive understanding of the XGBoost algorithm and its benefitsImplement XGBoost using the Python API for practical applicationsEvaluate model performance using appropriate metricsDeploy XGBoost models into production environmentsHandle complex datasets and extract valuable insightsGain practical experience in feature engineering, feature selection, and categorical encodingWho this book is for
This book is for data scientists, machine learning practitioners, analysts, and professionals interested in predictive modeling and time series analysis. Basic coding knowledge and familiarity with Python, GitHub, and other DevOps tools are required.
Table of ContentsAn Overview of Machine Learning, Classification, and RegressionXGBoost Quick Start Guide with an Iris Data Case StudyDemystifying the XGBoost PaperAdding On to the Quick Start - Switching Out the Dataset with a Housing Data Case StudyClassification and Regression Trees, Ensembles, and Deep Learning Models - What's Best for Your Data?Data Cleaning, Imbalanced Data, and Other Data ProblemsFeature EngineeringEncoding Techniques for Categorical FeaturesUsing XGBoost for Time Series ForecastingModel Interpretability, Explainability, and Feature Importance with XGBoostMetrics for Model Evaluations and ComparisonsManaging a Feature Engineering Pipeline in Training and InferenceDeploying Your XGBoost Model

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