Wade Hands-On Gradient Boosting with XGBoost and scikit-learn

Perform accessible machine learning and extreme gradient boosting with Python
1. Auflage 2020
ISBN: 978-1-83921-380-9
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 310 Seiten

No detailed description available for "Hands-On Gradient Boosting with XGBoost and scikit-learn".

Autoren/Hrsg.

Wade, Corey

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke

Inhaltsverzeichnis

Table of Contents - Machine Learning Landscape
- Decision Trees in Depth
- Bagging with Random Forests
- From Gradient Boosting to XGBoost
- XGBoost Unveiled
- XGBoost Hyperparameters
- Discovering Exoplanets with XGBoost
- XGBoost Alternative Base Learners
- XGBoost Kaggle Masters
- XGBoost Model Deployment

Über Autor(innen)

Wade Corey:

Corey Wade, M.S. Mathematics, M.F.A. Writing & Consciousness, is the founder and director of Berkeley Coding Academy where he teaches Machine Learning and AI to teens from all over the world. Additionally, Corey chairs the Math Department at Berkeley Independent Study where he has received multiple grants to run after-school coding programs to help bridge the tech skills gap. Additional experiences include teaching Natural Language Processing with Hello World, developing Data Science curricula with Pathstream, and publishing statistics and machine learning models with Towards Data Science, Springboard, and Medium.