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  • Hands-on gradient boosting with XGBoost and scikit-learn : Perform accessible machine learning and extreme gradient boosting with python
  • Tác giả: Corey Wade
  • Nhà xuất bản: Packt Publishing - Birmingham
  • Năm xuất bản: 2020
  • Số trang:282 p.
  • Kích thước:24 cm.
  • Số đăng ký cá biệt:48619
  • ISBN:9781839218354
  • Mã Dewey:006.31
  • Đơn giá:0
  • Vị trí lưu trữ:7/25 Quang Trung
  • Ngôn ngữ:English
  • Loại tài liệu:Giáo trình
  • Đang rỗi/ Tổng sách:1/1
  • Từ khóa:XGBoost, scikit-learn, machine learning, Python
  • Chủ đề: Python (Computer program language) & Machine learning
  • Chuyên ngành: Khoa Công Nghệ Thông Tin
  • Tóm tắt: 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.
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