CRAN . RSTUDIO . COM {}

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We are analyzing https://cran.rstudio.com/web/packages/xgboost/index.html.

Title:
CRAN: Package xgboost
Description:
Extreme Gradient Boosting, which is an efficient implementation of the gradient boosting framework from Chen & Guestrin (2016) <<a href="https://doi.org/10.1145%2F2939672.2939785" target="_top">doi:10.1145/2939672.2939785</a>>. This package is its R interface. The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation on a single machine which could be more than 10 times faster than existing gradient boosting packages. It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that users are also allowed to define their own objectives easily.
Website Age:
27 years and 1 months (reg. 1998-05-15).

Matching Content Categories {📚}

Shopping
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Content Management System {📝}

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Custom-built

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Traffic Estimate {📈}

What is the average monthly size of cran.rstudio.com audience?

🚀 Good Traffic: 50k - 100k visitors per month

Based on our best estimate, this website will receive around 50,019 visitors per month in the current month.
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How Does Cran.rstudio.com Make Money? {💸}

We're unsure if the website is profiting.

While many websites aim to make money, others are created to share knowledge or showcase creativity. People build websites for various reasons. This could be one of them. Cran.rstudio.com might be cashing in, but we can't detect the method they're using.

Keywords {🔍}

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Topics {✒️}

extreme gradient boosting /dmlc/xgboost/issues license gradient boosting framework tree learning algorithms file license url gz windows binaries xaitest reverse enhances /dmlc/xgboost needscompilation michael benesty [aut] vadim khotilovich [aut] hyunsu cho [aut] rory mitchell [aut] ignacio cano [aut] tianyi zhou [aut] mu li [aut] junyuan xie [aut] min lin [aut] yifeng geng [aut] yutian li [aut] tianqi chen [aut] kailong chen [aut] xgboost contributors [cph] xgboost results documentation jiaming yuan [aut pie reverse imports xrf reverse suggests base xgboost implementation survival cran checks zip macos binaries canonical form https org/package=xgboost efficient implementation apache license chen & guestrin reverse depends xgboost author xgboost presentation parallel computation single machine 10 times faster objective functions including regression objectives easily titanic published gnu make reference manual pdf vignettes r-devel r-release r-oldrel

External Links {🔗}(21)

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