XGBoost for Regression Predictive Modeling and Time Series Analysis : Learn How to Build, Evaluate, and Deploy Predictive Models with Expert Guidance.

Format:

Book

Author/Creator:

Deka, Partha Pritam.

Contributor:

Weiner, Joyce.

Zicari, Prof. Roberto V.

Language:

English

Subjects (All):

Machine learning.

Regression analysis--Data processing.

Regression analysis.

Time-series analysis--Data processing.

Time-series analysis.

Python (Computer program language).

Physical Description:

1 online resource (308 pages)

Edition:

1st ed.

Place of Publication:

Birmingham : Packt Publishing, Limited, 2024.

Biography/History:

Deka Partha Pritam: Partha Pritam Deka is a data science leader with 15+ years of experience in semiconductor supply chain and manufacturing. As a senior staff engineer at Intel, he has led AI and machine learning teams, achieving significant cost savings and optimizations. He and his team developed a computer vision system that improved Intel's logistics, earning CSCMP Innovation Award finalist recognition. An active AI community member, Partha is a senior IEEE member and speaker at Intel's AI Everywhere conference. He also reviews for NeurIPS, contributing to AI and analytics in semiconductor manufacturing. Weiner Joyce: Joyce Weiner is a principal engineer with Intel Corporation. She has over 25 years of experience in the semiconductor industry, having worked in fabrication, assembly and testing, and design. Since the early 2000s, she has deployed applications that use machine learning. Joyce is a black belt in Lean Six Sigma and her area of technical expertise is the application of data science to improve efficiency. She has a BS in Physics from Rensselaer Polytechnic Institute and an MS in Optical Sciences from the University of Arizona.

Summary:

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 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.

Contents:

XGBoost for Regression Predictive Modeling and Time Series Analysis: Learn how to build, evaluate, and deploy predictive models with expert guidance

Notes:

Includes index.

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Deka, Partha Pritam XGBoost for Regression Predictive Modeling and Time Series Analysis

ISBN:

9781805129608

OCLC:

1472148234