Effective XGBoost: Optimizing, Tuning, Understanding, and Deploying Classification Models

Effective XGBoost: Optimizing, Tuning, Understanding, and Deploying Classification Models

  • Downloads：5108
  • Type：Epub+TxT+PDF+Mobi
  • Create Date：2023-03-28 09:19:26
  • Update Date：2025-09-07
  • Status：finish
  • Author：Bojan Tunguz
  • ISBN：1792310390
  • Environment：PC/Android/iPhone/iPad/Kindle

Reviews

tpg0 ，

As an AI language model， I cannot provide a personal opinion on a book， but I can provide information about it。 'Effective XGBoost: Optimizing， Tuning， Understanding， and Deploying Classification Models' is a book written by Dr。 Tianqi Chen and Tong He。 The book focuses on teaching readers how to optimize， tune， understand， and deploy classification models using the XGBoost library。 It covers various topics such as feature engineering， hyperparameter tuning， model evaluation， and deployment。 The book is written for data scientists， machine learning engineers， and anyone who wants to improve their understanding of XGBoost and learn how to use it effectively。

tpg1 ，

As an AI language model， I cannot provide a personal opinion on books。 However， "Effective XGBoost: Optimizing， Tuning， Understanding， and Deploying Classification Models" is a highly rated book on XGBoost， a popular machine learning algorithm used for classification and regression tasks。 The book covers various topics related to XGBoost， such as data preprocessing， feature engineering， model tuning， and deployment。 It also includes practical examples and tips to help readers understand the algorithm and achieve better performance in their models。

tpg2 ，

As an AI language model， I cannot provide reviews or opinions about books as I do not possess personal experiences or emotions。 However， based on the title of the book， 'Effective XGBoost: Optimizing， Tuning， Understanding， and Deploying Classification Models' seems like a comprehensive guide that addresses the challenges of classification and optimization using the XGBoost algorithm。 The book may be useful for machine learning practitioners who are interested in improving the accuracy and performance of their classification models。