Deka / Weiner XGBoost for Regression Predictive Modeling and Time Series Analysis

Learn how to build, evaluate, and deploy predictive models with expert guidance
1. Auflage 2025
ISBN: 978-1-80512-960-8
Verlag: De Gruyter
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 308 Seiten

No detailed description available for "XGBoost for Regression Predictive Modeling and Time Series Analysis".

Autoren/Hrsg.

Deka, Partha Pritam

Weiner, Joyce

Weitere Mitwirkende

Zicari, Prof. Roberto V.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Technische Informatik Eingebettete Systeme
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Table of Contents - An Overview of Machine Learning, Classification, and Regression
- XGBoost Quick Start Guide with an Iris Data Case Study
- Demystifying the XGBoost Paper
- Adding On to the Quick Start – Switching Out the Dataset with a Housing Data Case Study
- Classification and Regression Trees, Ensembles, and Deep Learning Models – What's Best for Your Data?
- Data Cleaning, Imbalanced Data, and Other Data Problems
- Feature Engineering
- Encoding Techniques for Categorical Features
- Using XGBoost for Time Series Forecasting
- Model Interpretability, Explainability, and Feature Importance with XGBoost
- Metrics for Model Evaluations and Comparisons
- Managing a Feature Engineering Pipeline in Training and Inference
- Deploying Your XGBoost Model

Über Autor(innen)

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.