Implementando Gradient Boosting Programado Em Python
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Implementando Gradient Boosting Programado Em Python
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Author : Vitor Amadeu Souza
language : pt-BR
Publisher: Clube de Autores
Release Date : 2025-07-07
Implementando Gradient Boosting Programado Em Python written by Vitor Amadeu Souza and has been published by Clube de Autores this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-07 with Computers categories.
O avanço do aprendizado de máquina transformou profundamente a maneira como lidamos com dados, tomamos decisões automatizadas e desenvolvemos sistemas inteligentes. Entre as técnicas mais eficazes e versáteis desse campo, destaca-se o Gradient Boosting, um poderoso método de ensemble que combina diversas árvores de decisão fracas para formar um modelo robusto e altamente preciso. Este livro apresenta uma abordagem prática e objetiva para quem deseja compreender e aplicar o Gradient Boosting em problemas de classificação usando Python. Com base em um exemplo claro e funcional, o leitor aprenderá como gerar conjuntos de dados sintéticos, treinar um modelo com o GradientBoostingClassifier do scikit-learn, fazer previsões e avaliar os resultados por meio de métricas como acurácia e matriz de confusão. O conteúdo é acessível tanto para iniciantes quanto para profissionais que desejam reforçar seus conhecimentos em técnicas supervisionadas de machine learning.
Practical Gradient Boosting
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Author : Guillaume Saupin
language : en
Publisher: guillaume saupin
Release Date : 2022-11-10
Practical Gradient Boosting 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 2022-11-10 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.
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.
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...
Implementando Xgboost Programado Em Python
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Author : Vitor Amadeu Souza
language : pt-BR
Publisher: Clube de Autores
Release Date : 2025-07-07
Implementando Xgboost Programado Em Python written by Vitor Amadeu Souza and has been published by Clube de Autores this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-07 with Computers categories.
O avanço das técnicas de aprendizado de máquina tem revolucionado a análise de dados e a tomada de decisões automatizadas em diversas áreas do conhecimento e setores da indústria. Dentre os métodos mais eficazes para problemas de classificação e regressão, o XGBoost se destaca como um algoritmo poderoso e versátil, baseado na técnica de boosting por gradiente. Este livro apresenta uma abordagem prática e didática para compreender e aplicar o XGBoost, utilizando a linguagem Python e suas bibliotecas mais populares.
Aprendizado De M Quina Supervisionado Utilizando Gbc Programado Em Python
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Author : Vitor Amadeu Souza
language : pt-BR
Publisher: Clube de Autores
Release Date : 2024-09-16
Aprendizado De M Quina Supervisionado Utilizando Gbc Programado Em Python written by Vitor Amadeu Souza and has been published by Clube de Autores this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-16 with Computers categories.
Este livro visa introduzir e explorar o conceito de Classificação utilizando Gradient Boosting Classifier (GBC), aplicado a um cenário prático usando Python. O foco está em ensinar como utilizar esse algoritmo de aprendizado supervisionado para resolver problemas de classificação. Em particular, abordamos um exemplo prático de classificação de flores utilizando o famoso conjunto de dados Iris, demonstrando como o algoritmo GBC pode ser usado para prever espécies de plantas com base em suas características morfológicas.
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
Gradient Boosting In Automatic Machine Learning Feature Selection And Hyperparameter Optimization
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Author : Janek Thomas
language : en
Publisher:
Release Date : 2019
Gradient Boosting In Automatic Machine Learning Feature Selection And Hyperparameter Optimization written by Janek Thomas 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.
Xgboost The Extreme Gradient Boosting For Mining Applications
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Author : Nonita Sharma
language : en
Publisher: GRIN Verlag
Release Date : 2018-03-13
Xgboost The Extreme Gradient Boosting For Mining Applications written by Nonita Sharma and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-13 with Computers categories.
Technical Report from the year 2017 in the subject Computer Science - Internet, New Technologies, grade: 8, , language: English, abstract: Tree boosting has empirically proven to be a highly effective and versatile approach for data-driven modelling. The core argument is that tree boosting can adaptively determine the local neighbourhoods of the model thereby taking the bias-variance trade-off into consideration during model fitting. Recently, a tree boosting method known as XGBoost has gained popularity by providing higher accuracy. XGBoost further introduces some improvements which allow it to deal with the bias-variance trade-off even more carefully. In this research work, we propose to demonstrate the use of an adaptive procedure i.e. Learned Loss (LL) to update the loss function as the boosting proceeds. Accuracy of the proposed algorithm i.e. XGBoost with Learned Loss boosting function is evaluated using test/train method, K-fold cross validation, and Stratified cross validation method and compared with the state of the art algorithms viz. XGBoost, AdaBoost, AdaBoost-NN, Linear Regression(LR),Neural Network(NN), Decision Tree(DT), Support Vector Machine(SVM), bagging-DT, bagging-NN and Random Forest algorithms. The parameters evaluated are accuracy, Type 1 error and Type 2 error (in Percentages). This study uses total ten years of historical data from Jan 2007 to Aug 2017 of two stock market indices CNX Nifty and S&P BSE Sensex which are highly voluminous. Further, in this research work, we will investigate how XGBoost differs from the more traditional ensemble techniques. Moreover, we will discuss the regularization techniques that these methods offer and the effect these have on the models. In addition to this, we will attempt to answer the question of why XGBoost seems to win so many competitions. To do this, we will provide some arguments for why tree boosting, and in particular XGBoost, seems to be such a highly effective and versatile approach to predictive modelling. The core argument is that tree boosting can be seen to adaptively determine the local neighbourhoods of the model. Tree boosting can thus be seen to take the bias-variance trade off into consideration during model fitting. XGBoost further introduces some improvements which allow it to deal with the bias-variance trade off even more carefully.