Xgboost For Regression Predictive Modeling And Time Series Analysis
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Xgboost For Regression Predictive Modeling And Time Series Analysis
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Author : Partha Pritam Deka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-12-13
Xgboost For Regression Predictive Modeling And Time Series Analysis written by Partha Pritam Deka and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-13 with Computers categories.
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 Features Get up and running with this quick-start guide to building a classifier using XGBoost Get an easy-to-follow, in-depth explanation of the XGBoost technical paper Leverage XGBoost for time series forecasting by using moving average, frequency, and window methods Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionXGBoost 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.What you will learn Build a strong, intuitive understanding of the XGBoost algorithm and its benefits Implement XGBoost using the Python API for practical applications Evaluate model performance using appropriate metrics Deploy XGBoost models into production environments Handle complex datasets and extract valuable insights Gain practical experience in feature engineering, feature selection, and categorical encoding Who 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.
Xgboost For Regression Predictive Modeling And Time Series Analysis
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Author : Joyce Weiner
language : en
Publisher: Packt Publishing
Release Date : 2024-12-13
Xgboost For Regression Predictive Modeling And Time Series Analysis written by Joyce Weiner and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-13 with Computers categories.
XGBoost for Regression Predictive Modelling and Time Series Analysis will help you get a practical understanding of the XGBoost algorithm.
Hands On Gradient Boosting With Xgboost And Scikit Learn
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Author : Corey Wade
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-10-16
Hands On Gradient Boosting With Xgboost And Scikit Learn written by Corey Wade and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-16 with Computers categories.
Get to grips with building robust XGBoost models using Python and scikit-learn for deployment Key Features Get up and running with machine learning and understand how to boost models with XGBoost in no time Build real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal results Discover tips and tricks and gain innovative insights from XGBoost Kaggle winners Book Description XGBoost is an industry-proven, open-source software library that provides a gradient boosting framework for scaling billions of data points quickly and efficiently. The book introduces machine learning and XGBoost in scikit-learn before building up to the theory behind gradient boosting. You'll cover decision trees and analyze bagging in the machine learning context, learning hyperparameters that extend to XGBoost along the way. You'll build gradient boosting models from scratch and extend gradient boosting to big data while recognizing speed limitations using timers. Details in XGBoost are explored with a focus on speed enhancements and deriving parameters mathematically. With the help of detailed case studies, you'll practice building and fine-tuning XGBoost classifiers and regressors using scikit-learn and the original Python API. You'll leverage XGBoost hyperparameters to improve scores, correct missing values, scale imbalanced datasets, and fine-tune alternative base learners. Finally, you'll apply advanced XGBoost techniques like building non-correlated ensembles, stacking models, and preparing models for industry deployment using sparse matrices, customized transformers, and pipelines. By the end of the book, you'll be able to build high-performing machine learning models using XGBoost with minimal errors and maximum speed. What you will learn Build gradient boosting models from scratch Develop XGBoost regressors and classifiers with accuracy and speed Analyze variance and bias in terms of fine-tuning XGBoost hyperparameters Automatically correct missing values and scale imbalanced data Apply alternative base learners like dart, linear models, and XGBoost random forests Customize transformers and pipelines to deploy XGBoost models Build non-correlated ensembles and stack XGBoost models to increase accuracy Who this book is for This book is for data science professionals and enthusiasts, data analysts, and developers who want to build fast and accurate machine learning models that scale with big data. Proficiency in Python, along with a basic understanding of linear algebra, will help you to get the most out of this book.
Proceedings Of The International Field Exploration And Development Conference 2024
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Author : Jia'en Lin
language : en
Publisher: Springer Nature
Release Date : 2025-07-07
Proceedings Of The International Field Exploration And Development Conference 2024 written by Jia'en Lin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-07 with Technology & Engineering categories.
This book compiles selected papers from the 14th International Field Exploration and Development Conference (IFEDC 2024). The work focuses on topics including Reservoir Exploration, Reservoir Drilling & Completion, Field Geophysics, Well Logging, Petroliferous Basin Evaluation, Oil & Gas Accumulation, Fine Reservoir Description, Complex Reservoir Dynamics and Analysis, Low Permeability/Tight Oil & Gas Reservoirs, Shale Oil & Gas, Fracture-Vuggy Reservoirs, Enhanced Oil Recovery in Mature Oil Fields, Enhanced Oil Recovery for Heavy Oil Reservoirs, Big Data and Artificial Intelligence, Formation Mechanisms and Prediction of Deep Carbonate Reservoirs, and other Unconventional Resources. The conference serves as a platform not only for exchanging experiences but also for advancing scientific research in oil & gas exploration and production. The primary audience for this work includes reservoir engineers, geological engineers, senior engineers, enterprise managers, and students.
Deep Learning Concepts In Operations Research
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Author : Biswadip Basu Mallik
language : en
Publisher: CRC Press
Release Date : 2024-08-30
Deep Learning Concepts In Operations Research written by Biswadip Basu Mallik and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-30 with Computers categories.
The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the machine learning paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and ML as well. Among a variety of topics, the book examines: An overview of applications and computing devices Deep learning impacts in the field of AI Deep learning as state-of-the-art approach to AI Exploring deep learning architecture for cutting-edge AI solutions Operations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.
Evolutionary Artificial Intelligence
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Author : David Asirvatham
language : en
Publisher: Springer Nature
Release Date : 2025-07-25
Evolutionary Artificial Intelligence written by David Asirvatham and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-25 with Computers categories.
This book gathers a collection of selected works and new research results of scholars and graduate students presented at International Conference on Evolutionary Artificial Intelligence (ICEAI 2024) held in Malaysia during November 26–27, 2024. The focus of the book is interdisciplinary in nature and includes research on all aspects of evolutionary computation to find effective solutions to a wide range of computationally difficult problems. The book covers topics such as particle swarm optimization, evolutionary programming, genetic programming, hybrid evolutionary algorithms, ant colony optimization, evolutionary neural networks, evolutionary reinforcement learning, genetic algorithms, memetic algorithms, novel bio-inspired algorithms, evolving multi-agent systems, agent-based evolutionary approaches, and evolutionary game theory.
Big Data And Data Science Engineering
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Author : Roger Lee
language : en
Publisher: Springer Nature
Release Date : 2025-04-30
Big Data And Data Science Engineering written by Roger Lee and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-30 with Computers categories.
This book reports state-of-the-art results in Big Data and Data Science Engineering in both printed and electronic form. Studies in Computation Intelligence (SCI) has grown into the most comprehensive computational intelligence research forum available in the world. This book publishes original papers on both theory and practice that address foundations, state-of-the-art problems and solutions, and crucial challenges.
Intelligent Techniques For Predictive Data Analytics
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Author : Neha Singh
language : en
Publisher: John Wiley & Sons
Release Date : 2024-06-21
Intelligent Techniques For Predictive Data Analytics written by Neha Singh and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-21 with Computers categories.
Comprehensive resource covering tools and techniques used for predictive analytics with practical applications across various industries Intelligent Techniques for Predictive Data Analytics provides an in-depth introduction of the tools and techniques used for predictive analytics, covering applications in cyber security, network security, data mining, and machine learning across various industries. Each chapter offers a brief introduction on the subject to make the text accessible regardless of background knowledge. Readers will gain a clear understanding of how to use data processing, classification, and analysis to support strategic decisions, such as optimizing marketing strategies and customer relationship management and recommendation systems, improving general business operations, and predicting occurrence of chronic diseases for better patient management. Traditional data analytics uses dashboards to illustrate trends and outliers, but with large data sets, this process is labor-intensive and time-consuming. This book provides everything readers need to save time by performing deep, efficient analysis without human bias and time constraints. A section on current challenges in the field is also included. Intelligent Techniques for Predictive Data Analytics covers sample topics such as: Models to choose from in predictive modeling, including classification, clustering, forecast, outlier, and time series models Price forecasting, quality optimization, and insect and disease plant and monitoring in agriculture Fraud detection and prevention, credit scoring, financial planning, and customer analytics Big data in smart grids, smart grid analytics, and predictive smart grid quality monitoring, maintenance, and load forecasting Management of uncertainty in predictive data analytics and probable future developments in the field Intelligent Techniques for Predictive Data Analytics is an essential resource on the subject for professionals and researchers working in data science or data management seeking to understand the different models of predictive analytics, along with graduate students studying data science courses and professionals and academics new to the field.
Proceedings Of The 2024 2nd International Conference On Finance Trade And Business Management Ftbm 2024
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Author : Amalendu Bhunia
language : en
Publisher: Springer Nature
Release Date : 2024-10-26
Proceedings Of The 2024 2nd International Conference On Finance Trade And Business Management Ftbm 2024 written by Amalendu Bhunia and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-26 with Business & Economics categories.
This book is open access FTBM 2024 will be held in Hangzhou, China during August 23-25, 2024. The conference will focus on the Finance, Trade and Business Management, discuss the key challenges and research directions faced by the development of this field, in order to promote the development and application of theories and technologies in this field in universities and enterprises, and provide innovative scholars who focus on this research field, engineers and industry experts provide a favorable platform for exchanging new ideas and presenting research results. Internet of Things Planned highlights of FTBM 2024 include: ● Addresses and presentations by some of the most respected researchers in the Finance, Trade and Business Management ● Panel discussions ● Presentations of accepted academic and practitioner research papers; a poster paper session
Global Covid 19 Research And Modeling
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Author : Longbing Cao
language : en
Publisher: Springer Nature
Release Date : 2024-03-29
Global Covid 19 Research And Modeling written by Longbing Cao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-29 with Computers categories.
This book provides answers to fundamental and challenging questions regarding the global response to COVID-19. It creates a historical record of COVID-19 research conducted over the four years of the pandemic, with a focus on how researchers have responded, quantified, and modeled COVID-19 problems. Since mid-2021, we have diligently monitored and analyzed global scientific efforts in tackling COVID-19. Our comprehensive global endeavor involves collecting, processing, analyzing, and discovering COVID-19 related scientific literature in English since January 2020. This provides insights into how scientists across disciplines and almost every country and regions have fought against COVID-19. Additionally, we explore the quantification of COVID-19 problems and impacts through mathematics, AI, machine learning, data science, epidemiology, and domain knowledge. The book reports findings on publication quantities, impacts, collaborations, and correlations with the economy and infectionsglobally, regionally, and country-wide. These results represent the first and only holistic and systematic studies aimed at scientifically understanding, quantifying, and containing the pandemic. We hope this comprehensive analysis will contribute to better preparedness, response, and management of future emergencies and inspire further research in infectious diseases. The book also serves as a valuable resource for research policy, funding management authorities, researchers, policy makers, and funding bodies involved in infectious disease management, public health, and emergency resilience.