Xgboost With Python
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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.
Xgboost With Python
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2016-08-05
Xgboost With Python written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-05 with Computers categories.
XGBoost is the dominant technique for predictive modeling on regular data. The gradient boosting algorithm is the top technique on a wide range of predictive modeling problems, and XGBoost is the fastest implementation. When asked, the best machine learning competitors in the world recommend using XGBoost. In this Ebook, learn exactly how to get started and bring XGBoost to your own machine learning projects.
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.
Hands On Gradient Boosting With Python
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Author : Dr Adrian Devlin
language : en
Publisher: Independently Published
Release Date : 2025-12-11
Hands On Gradient Boosting With Python written by Dr Adrian Devlin and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-11 with Computers categories.
Are you curious about machine learning but feel overwhelmed by math, jargon, and complex tutorials? If words like XGBoost, LightGBM, and gradient boosting sound exciting but intimidating, this book is your friendly guide through the noise. Hands-On Gradient Boosting with Python: A Practical Introduction to XGBoost, LightGBM, and the Scikit-Learn Ecosystem is written for complete beginners and self-taught developers who want a clear, step-by-step path into modern Python machine learning-without needing a PhD or years of coding experience. You'll start with the basics of Python, scikit-learn, and tabular data, then gently build up to powerful boosting models used in real-world projects and Kaggle competitions. Every chapter walks you through code line by line, explains why each step matters, and shows you how to avoid common mistakes. Inside, you'll learn how to: Set up your Python machine learning environment with confidence Understand core concepts like decision trees, ensembles, and gradient boosting in plain English Build practical models with scikit-learn, XGBoost, and LightGBM for regression and classification Work on real-world projects such as house price prediction and credit risk scoring Tune hyperparameters, handle imbalanced data, and evaluate models with metrics like AUC, F1, and RMSE Use SHAP and LIME for model explainability so you can trust your predictions Save, load, and deploy your models so they are ready for real applications Throughout the book, you're treated like a learner-not a walking error message. Mistakes are normalized, experiments are encouraged, and every "small win" is celebrated: Clear explanations before any code Gradual progression from simple to advanced models Gentle reminders that confusion is part of learning Practical tips for debugging, improving, and reusing your work Whether you're a student, an aspiring data scientist, or a developer stepping into Python machine learning for the first time, this book becomes your supportive companion-one that makes gradient boosting feel approachable, understandable, and genuinely fun. If you're ready to stop scrolling tutorials and start building real models that actually work, open this book and begin your hands-on journey into gradient boosting with Python today.
Machine Learning Series
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Author : Dhiraj Kumar
language : en
Publisher:
Release Date : 2019
Machine Learning Series written by Dhiraj Kumar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Dhiraj, a data scientist and machine learning evangelist, continues his teaching of machine learning algorithms by explaining through both lecture and practice the XGBoost (eXtreme Gradient Boosting) Algorithm in Python. Click here to watch all of Dhiraj Kumar's machine learning videos . Learn all about XGBoost using Python and the Jupyter notebook in this video series covering these seven topics: Introducing XGBoost . This first topic in the XGBoost (eXtreme Gradient Boosting) Algorithm in Python series introduces this very important machine learning algorithm. Gradient boosting is a machine learning technique for regression and classification problems. Learn about the reasons for using XGBoost, including accuracy, speed, and scale. Understand ensemble modeling and how it can improve the overall performance of a machine learning model. Apply the concepts of bagging and boosting, and learn about AdaBoost and Gradient boosting. XGBoost Benefits . This second topic in the XGBoost Algorithm in Python series covers where XGBoost works well. XGBoost guarantees regularization (which prevents the model from overfitting), supports parallel processing, provides a built-in capacity for handling missing values, and excels at tree pruning and cross validation. Installing XGBoost . This third topic in the XGBoost Algorithm in Python series covers how to install the XGBoost library. It is recommended to be using Python 64 bit. Become proficient in installing Anaconda and the XGBoost library on Windows, Linux, and Mac OS. XGBoost Model Implementation in Python . This fourth topic in the XGBoost Algorithm in Python series covers how to implement the various XGBoost linear and tree learning models in Python. Practice applying the XGBoost models using a medical data set. XGBoost Parameter Tuning in Python . This fifth topic in the XGBoost Algorithm in Python series covers how to tune the various parameters that exist in Python. Parameter tuning is the art in machine learning. Follow along and practice applying the three categories of parameter tuning: Tree Parameters, Boosting Parameters, and Other Parameters. Become proficient in a number of parameters including max_depth, min_samples_leaf, and max_features, XGBoost Model Evaluation Method in Python . This sixth topic in the XGBoost Algorithm in Python series shows you how to evaluate an XGBoost model. Follow along and practice applying the two most important techniques of Train Test Split and Cross Validatio...
Practical Xgboost
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Author : Everett Higgins
language : en
Publisher: Independently Published
Release Date : 2025-10-25
Practical Xgboost written by Everett Higgins and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-25 with Computers categories.
Practical XGBoost is a hands-on, step-by-step guide for mastering one of the most powerful machine learning algorithms available today. This book shows how to build, tune, and deploy gradient boosting models using Python, making complex concepts approachable and actionable for anyone working with data. Inside, you'll discover how to transform tabular data into predictive models that deliver real results. From handling missing values to engineering meaningful features, optimizing hyperparameters, and scaling models for large datasets, every concept is paired with runnable Python examples that bring learning directly into practice. What this book helps you achieve: Build accurate classification and regression models efficiently using XGBoost. Engineer features and interpret model predictions for clear, trustworthy results. Tune hyperparameters, implement distributed training, and leverage GPU acceleration. Deploy models in real-world pipelines using FastAPI, MLflow, and cloud platforms. Explore practical case studies in finance, healthcare, and energy to see XGBoost applied to real problems. This book stands out by focusing on practical, end-to-end implementation. Every step is explained clearly, with actionable tips and code that works out of the box, bridging the gap between understanding the algorithm and applying it in production-ready systems. Take control of your machine learning projects. Start building faster, smarter, and more accurate models with Practical XGBoost.
Practical Xgboost
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Author : Everett Higgins
language : en
Publisher: Independently Published
Release Date : 2025-10-25
Practical Xgboost written by Everett Higgins and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-25 with Computers categories.
Practical XGBoost is a hands-on, step-by-step guide for mastering one of the most powerful machine learning algorithms available today. This book shows how to build, tune, and deploy gradient boosting models using Python, making complex concepts approachable and actionable for anyone working with data. Inside, you'll discover how to transform tabular data into predictive models that deliver real results. From handling missing values to engineering meaningful features, optimizing hyperparameters, and scaling models for large datasets, every concept is paired with runnable Python examples that bring learning directly into practice. What this book helps you achieve: Build accurate classification and regression models efficiently using XGBoost. Engineer features and interpret model predictions for clear, trustworthy results. Tune hyperparameters, implement distributed training, and leverage GPU acceleration. Deploy models in real-world pipelines using FastAPI, MLflow, and cloud platforms. Explore practical case studies in finance, healthcare, and energy to see XGBoost applied to real problems. This book stands out by focusing on practical, end-to-end implementation. Every step is explained clearly, with actionable tips and code that works out of the box, bridging the gap between understanding the algorithm and applying it in production-ready systems. Take control of your machine learning projects. Start building faster, smarter, and more accurate models with Practical XGBoost.
Python Programming
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Author :
language : en
Publisher:
Release Date : 2025-05-10
Python Programming written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-10 with Computers categories.
Preface In recent years, Machine Learning and Data Science have revolutionized the way we understand and interact with data. From predictive analytics in finance and healthcare to real-time recommendation systems in e-commerce and streaming platforms, intelligent algorithms are now an integral part of the modern digital landscape. This book, "Machine Learning & Data Science: TensorFlow, PyTorch, XGBoost, Statsmodels," is crafted for learners and practitioners who aim to bridge the gap between theory and hands-on application using some of the most powerful tools in the industry. The rapid expansion of available data and computational power has made it possible to deploy increasingly complex models. However, success in this field requires more than just technical proficiency-it demands an understanding of the appropriate frameworks, their strengths, and the contexts in which they excel. This book is structured to serve that purpose. We explore TensorFlow and PyTorch, the two most widely adopted deep learning frameworks, each with its own philosophy and design choices. TensorFlow, with its scalable ecosystem and production-oriented approach, is ideal for building deployable machine learning systems. PyTorch, known for its intuitive design and dynamic computation graphs, is a favorite in the research community and for rapid prototyping. In contrast, XGBoost represents the pinnacle of gradient boosting techniques-efficient, scalable, and often the go-to choice for structured data and tabular modeling competitions. And then there's Statsmodels, a library that brings the richness of statistical modeling into the mix, enabling interpretability and insight that purely algorithmic models may lack. This book is designed with the following goals: To provide a comprehensive introduction to the foundational concepts of machine learning and data science. To illustrate practical implementations using TensorFlow, PyTorch, XGBoost, and Statsmodels through real-world examples and projects. To equip readers with the skills to choose and combine tools appropriately depending on the nature of the data and the problem at hand. To foster a deep understanding of not just how models work, but why they behave the way they do. Whether you are a student seeking to deepen your knowledge, a developer transitioning into the field, or a data scientist aiming to master additional tools, this book offers a balanced journey through both the statistical roots and the cutting-edge practices of machine learning. May this book serve not just as a manual, but as a roadmap in your data science journey-helping you think critically, implement confidently, and build responsibly. - The Author
The Gradient Boosting Guidebook
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Author : Haider Koele
language : en
Publisher: Independently Published
Release Date : 2025-11-10
The Gradient Boosting Guidebook written by Haider Koele and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-10 with Computers categories.
Unlock the Power of Machine Learning-No Experience Needed! Are you curious about machine learning, but feel overwhelmed by jargon, complicated code, or fear that it's only for "experts"? The Gradient Boosting Guidebook is your friendly, step-by-step companion, crafted especially for beginners who want to confidently build real-world models using Python's most powerful tools-XGBoost, LightGBM, and Scikit-Learn. Imagine moving from confusion to clarity as you master gradient boosting, one of today's most important and in-demand techniques for data science and AI. Whether you dream of winning a Kaggle competition, landing a data science job, or simply understanding how modern predictions work, this book meets you exactly where you are-no prior programming or math background required. Inside, you'll discover: Crystal-Clear Explanations: Complex concepts like ensemble learning and model tuning are broken down into simple, friendly language anyone can understand. Hands-On Projects: Build practical machine learning solutions step by step, from data preparation and feature engineering to model deployment-perfect for portfolio-building or classroom use. Beginner-Friendly Python Tutorials: Get started fast, with easy instructions for installing and using the core Python ML libraries, even if you've never coded before. Real-World Applications: Work through guided projects that mirror real business and analytics challenges-like credit risk analysis, price prediction, and more. Troubleshooting and Cheat Sheets: Find quick help for common errors and reference guides to speed up your learning, reduce frustration, and celebrate every breakthrough. Supportive Tone: You'll find encouragement at every turn, with stories, tips, and "personal insight" that normalize mistakes and show you that learning is about growth, not perfection. Key Takeaways: Learn how to use gradient boosting to solve real problems with confidence Gain practical experience with XGBoost, LightGBM, and Scikit-Learn Master data cleaning, feature engineering, and hyperparameter tuning Build models that you can explain, deploy, and trust Embrace mistakes as part of the journey and celebrate each small win This isn't just a technical manual-it's your launchpad into the world of data science. If you've ever thought "I'm not technical enough," this guide is here to prove you wrong and show you just how capable you are. Every chapter builds your skills and confidence, guiding you from your very first model to deploying machine learning solutions you'll be proud of. Ready to turn uncertainty into expertise and make your mark in data science? Start your journey with The Gradient Boosting Guidebook and discover how approachable, practical, and empowering machine learning can be!
Scikit Learn Xgboost And Lightgbm For Beginners
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Author : Haider Koele
language : en
Publisher: Independently Published
Release Date : 2025-09-04
Scikit Learn Xgboost And Lightgbm For Beginners written by Haider Koele and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-04 with Computers categories.
Unlock the World of Machine Learning-No Experience Required! Do you feel overwhelmed by complex jargon and endless lines of code every time you think about learning machine learning? Have you wondered if you're "tech-savvy enough" to break into AI, or wished for a resource that makes the journey friendly, practical, and genuinely enjoyable? This is the beginner's book you've been searching for. Inside Scikit-Learn, XGBoost, and LightGBM for Beginners, you'll discover a warm, step-by-step guide that demystifies machine learning from the ground up-designed especially for readers with no prior experience. You don't need a computer science degree, advanced math skills, or a background in programming. All you need is curiosity, patience, and the desire to learn by doing. What Makes This Book Different? Gentle, Encouraging Approach: Every chapter is crafted to nurture your confidence and curiosity. Mistakes are normalized, progress is celebrated, and learning feels like an adventure-not a test. Step-by-Step Learning: Follow clear explanations and practical examples, from your very first line of Python code to building real machine learning projects with Scikit-Learn, XGBoost, and LightGBM. Hands-On Projects: Apply what you learn to real-world datasets and scenarios, so you gain skills that are both practical and relevant. Modern, In-Demand Tools: Master three of today's most popular frameworks-Scikit-Learn for accessible machine learning, XGBoost for powerful performance, and LightGBM for cutting-edge speed. No Experience Needed: Written with absolute beginners in mind, with jargon gently explained, plenty of support, and troubleshooting tips at every step. Complete Learning Journey: From setting up your environment, cleaning data, and building models to interpreting results and deploying solutions-you'll walk away with genuine confidence and tangible skills. Key Takeaways: Learn Python-based machine learning in plain language, with zero prior coding required Build, evaluate, and deploy real models using Scikit-Learn, XGBoost, and LightGBM Tackle practical projects that make your learning stick-classification, regression, pipelines, and more Overcome imposter syndrome with a nurturing, supportive tone that makes mistakes part of the process Prepare for real-world jobs, data science interviews, or personal projects with in-demand, industry-tested tools Find your place in the machine learning community-regardless of your background or experience Start Your Machine Learning Journey with Confidence! Whether your dream is to build smarter apps, break into data science, or simply understand the technology shaping our world, this book will guide you every step of the way. Embrace a hands-on, beginner-friendly approach that makes learning machine learning achievable, enjoyable, and even fun. Ready to transform confusion into confidence? Open this book, take your first step, and let your machine learning adventure begin!