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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Corey Wade | - |
dc.date.accessioned | 2023-05-17T04:21:30Z | - |
dc.date.available | 2023-05-17T04:21:30Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://thuvienso.thanglong.edu.vn//handle/TLU/6736 | - |
dc.description.abstract | 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. | vi |
dc.language.iso | en | vi |
dc.publisher | Birmingham, United Kingdom: Packt Publishing Limited | vi |
dc.subject | Python (Computer program language) | XGBoost | Ngôn ngữ chương trình máy tính | vi |
dc.title | Hands-On Gradient Boosting with XGBoost and scikit-learn | vi |
dc.title.alternative | Perform accessible machine learning and extreme gradient boosting with Python | vi |
dc.type | Sách/Book | vi |
Appears in Collections | Tin học |
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