Practical Gradient Boosting
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Practical Gradient Boosting A Deep Dive Into Gradient Boosting In Python
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Author : Guillaume Saupin
language : en
Publisher: guillaume saupin
Release Date : 2022-10-17
Practical Gradient Boosting A Deep Dive Into Gradient Boosting In Python written by Guillaume Saupin and has been published by guillaume saupin this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-17 with Computers categories.
This book on Gradient Boosting methods is intended for students, academics, engineers, and data scientists who wish to discover in depth the functioning of this Machine Learning technique used to build decision tree ensembles. All the concepts are illustrated by examples of application code. They allow the reader to rebuild from scratch his own training library of Gradient Boosting methods. In parallel, the book presents the best practices of Data Science and provides the reader with a solid technical background to build Machine Learning models. After a presentation of the principles of Gradient Boosting citing the application cases, advantages and limitations, the reader is introduced to the details of the mathematical theory. A simple implementation is given to illustrate how it works. The reader is then armed to tackle the application and configuration of these methods. Data preparation, training, explanation of a model, management of Hyper Parameter Tuning and use of objective functions are covered in detail! The last chapters of the book extend the subject to the application of Gradient Boosting for time series, the presentation of the emblematic libraries XGBoost, CatBoost and LightGBM as well as the concept of multi-resolution models.
Genetic Programming Theory And Practice Xiii
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Author : Rick Riolo
language : en
Publisher: Springer
Release Date : 2016-12-20
Genetic Programming Theory And Practice Xiii written by Rick Riolo and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-20 with Computers categories.
These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: multi-objective genetic programming, learning heuristics, Kaizen programming, Evolution of Everything (EvE), lexicase selection, behavioral program synthesis, symbolic regression with noisy training data, graph databases, and multidimensional clustering. It also covers several chapters on best practices and lesson learned from hands-on experience. Additional application areas include financial operations, genetic analysis, and predicting product choice. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.
Practical Predictive Analytics And Decisioning Systems For Medicine
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Author : Gary D. Miner
language : en
Publisher: Academic Press
Release Date : 2014-09-27
Practical Predictive Analytics And Decisioning Systems For Medicine written by Gary D. Miner and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-27 with Computers categories.
With the advent of electronic medical records years ago and the increasing capabilities of computers, our healthcare systems are sitting on growing mountains of data. Not only does the data grow from patient volume but the type of data we store is also growing exponentially. Practical Predictive Analytics and Decisioning Systems for Medicine provides research tools to analyze these large amounts of data and addresses some of the most pressing issues and challenges where data integrity is compromised: patient safety, patient communication, and patient information. Through the use of predictive analytic models and applications, this book is an invaluable resource to predict more accurate outcomes to help improve quality care in the healthcare and medical industries in the most cost–efficient manner.Practical Predictive Analytics and Decisioning Systems for Medicine provides the basics of predictive analytics for those new to the area and focuses on general philosophy and activities in the healthcare and medical system. It explains why predictive models are important, and how they can be applied to the predictive analysis process in order to solve real industry problems. Researchers need this valuable resource to improve data analysis skills and make more accurate and cost-effective decisions. - Includes models and applications of predictive analytics why they are important and how they can be used in healthcare and medical research - Provides real world step-by-step tutorials to help beginners understand how the predictive analytic processes works and to successfully do the computations - Demonstrates methods to help sort through data to make better observations and allow you to make better predictions
Lightgbm In Practice
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-11
Lightgbm In Practice written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-11 with Computers categories.
"LightGBM in Practice" "LightGBM in Practice" offers a comprehensive and authoritative exploration of one of the most powerful tools in the modern machine learning landscape. Beginning with in-depth coverage of LightGBM's foundational principles—such as gradient boosting decision trees, histogram-based learning, and innovative strategies for scalable performance—this book demystifies the underlying algorithms that fuel LightGBM’s speed and accuracy. Through clear explanations and mathematical rigor, readers will gain a deep understanding of both the theoretical and practical underpinnings that set LightGBM apart, including exclusive feature bundling, gradient-based sampling, and scalable system architecture. As the journey continues, "LightGBM in Practice" seamlessly bridges theory with real-world engineering. Readers will learn sophisticated data preparation and feature engineering techniques tailored for large-scale tabular and sparse datasets, discover best practices for distributed and GPU-accelerated training, and master advanced model optimization, hyperparameter tuning, and integration within enterprise ML pipelines. Dedicated chapters address model interpretability with industry-leading tools like SHAP and LIME, while also covering the nuances of regulatory compliance, auditability, and transparency—making the book indispensable for production-grade deployment in mission-critical environments. The final sections delve into specialized applications, operational strategies, and the future of LightGBM. From time series forecasting and recommendation engines to privacy preservation and fairness audits, "LightGBM in Practice" empowers practitioners to securely and robustly deploy, monitor, and scale models across diverse domains—including finance, health, and regulated industries. Complete with case studies, actionable code insights, and guidance for contributing to the open-source ecosystem, this essential guide ensures readers remain at the forefront of gradient boosting innovations for years to come.
Practical Statistical Learning And Data Science Methods
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Author : O. Olawale Awe
language : en
Publisher: Springer Nature
Release Date : 2024-12-27
Practical Statistical Learning And Data Science Methods written by O. Olawale Awe and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-27 with Computers categories.
This contributed volume offers practical implementation strategies for statistical learning and data science techniques, with fully peer-reviewed papers that embody insights and experiences gathered within the LISA 2020 Global Network. Through a series of compelling case studies, readers are immersed in practical methodologies, real-world applications, and innovative approaches in statistical learning and data science. Topics covered in this volume span a wide array of applications, including machine learning in health data analysis, deep learning models for precipitation modeling, interpretation techniques for machine learning models in BMI classification for obesity studies, as well as a comparative analysis of sampling methods in machine learning health applications. By addressing the evolving landscape of data analytics in many ways, this volume serves as a valuable resource for practitioners, researchers, and students alike. The LISA 2020 Global Network is dedicated to enhancing statistical and data science capabilities in developing countries through the establishment of collaboration laboratories, also known as “stat labs.” These stat labs function as engines for development, nurturing the next generation of collaborative statisticians and data scientists while providing essential research infrastructure for researchers, data producers, and decision-makers.
Practical Time Series Analysis
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Author : Aileen Nielsen
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-09-20
Practical Time Series Analysis written by Aileen Nielsen and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-20 with Computers categories.
Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance
Practical Machine Learning Applications
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Author : Dr. Sachin R. Jadhav
language : en
Publisher: Xoffencerpublication
Release Date : 2023-09-18
Practical Machine Learning Applications written by Dr. Sachin R. Jadhav and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-18 with Computers categories.
It is not feasible to arrive at an accurate estimate of the total quantity of knowledge that has been accumulated as a direct consequence of man's activity. Every single day, millions of new tuples are added to the databases, and each of those tuples represents an observation, an experience that can be learned from it, and a situation that may occur again in the future in a way that is comparable to the one it happened in when it was first observed. As human beings, we have the innate capacity to gain knowledge from our experiences, and this is something that occurs constantly throughout our lives. Nevertheless, what does place when the number of occurrences to which we are exposed is more than our capacity to comprehend each of them? What would happen if a fact were to be repeated millions of times, but it would never happen precisely the same way again? What would the results be? What kind of outcomes may we anticipate? It is a subfield of artificial intelligence that focuses on learning from experience, or, to be more specific, the process of automatically extracting implicit knowledge from information that is stored in the form of data. This subfield was named after the concept of learning from experience. Machine learning, which is sometimes shortened as ML and referred to in certain contexts as ML, is sometimes referred to simply as ML. In this study, we will investigate two problems that have been solved in the actual world of business by using machine learning. These problems were faced by companies throughout the globe. Companies were tasked with overcoming both of these obstacles. The first of these responsibilities is to provide an accurate forecast of the final product quality that will be supplied by an oil and gas refinery, which is discussed in Section 2. The second component is a model that, as will be covered in Section 3, may be used in order to acquire an estimate of the amount of wear and tear that will be experienced by a collection of micro gas turbines. This will be accomplished by calculating the amount of wear and tear that can be expected from the collection of micro gas turbines. In the phrase that follows, we will talk about the theoretical components that are essential for the creation of our solutions. An explanation of the ML approaches that we have used may be found in Section 1.1 for any reader who is interested in reading it and would want to read it.
Xgboost In Practice
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-05
Xgboost In Practice written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-05 with Computers categories.
"XGBoost in Practice" "XGBoost in Practice" delivers an authoritative and comprehensive exploration of one of modern machine learning’s most influential algorithms. Designed for data scientists, engineers, and professionals leveraging advanced analytics, this book demystifies the foundations of gradient boosting and the unique mechanics behind XGBoost. Readers are guided step-by-step through core principles, including additive modeling, regularization, handling missing data, and distinctions from alternative frameworks like LightGBM and CatBoost, laying a robust conceptual groundwork. The text goes far beyond theoretical exposition, emphasizing real-world data engineering, rigorous hyperparameter optimization, and advanced evaluation techniques. Each chapter equips readers with actionable methods for feature engineering, managing imbalanced and noisy datasets, distributed model training, and robust validation. Practical guidance on interpreting and explaining model predictions is underpinned by an in-depth treatment of explainability tools, fairness considerations, and strategies to monitor and maintain model performance in dynamic environments. Recognizing the demands of production systems and enterprise-scale deployments, "XGBoost in Practice" covers best practices for scalable deployment, MLOps pipelines, integration with big data platforms, and compliance with governance standards. Readers will also find rich domain-specific applications from fraud detection and time series forecasting to healthcare analytics and text classification. Culminating with chapters on extending XGBoost, GPU acceleration, and custom algorithm development, this book equips practitioners and technical leaders alike to produce, interpret, and operationalize state-of-the-art machine learning solutions with confidence.
Python For Data Analysis
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Author : Dr.Vidya Santosh Dhamdhere
language : en
Publisher: RK Publication
Release Date : 2024-07-25
Python For Data Analysis written by Dr.Vidya Santosh Dhamdhere and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-25 with Computers categories.
Python for Data Analysis the essential tools and techniques for data manipulation, cleaning, and analysis in Python. It emphasizes the use of libraries like pandas, NumPy, and Matplotlib to efficiently handle and visualize data. Ideal for analysts and aspiring data scientists, the book provides practical insights, examples, and workflows for handling real-world datasets. Whether for beginners or experienced professionals, it delivers a solid foundation in Python's data analysis ecosystem.
Data Science And Machine Learning For Non Programmers
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Author : Dothang Truong
language : en
Publisher: CRC Press
Release Date : 2024-02-23
Data Science And Machine Learning For Non Programmers written by Dothang Truong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-23 with Business & Economics categories.
As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively. Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders. Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds.