<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>xgboost on DataScience.LA</title><link>https://datasciencela.github.io/tag/xgboost/</link><description>Recent content in xgboost on DataScience.LA</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><copyright>DataScience.LA, 2014–2021 · Read-only archive: comments are closed.</copyright><lastBuildDate>Thu, 13 Oct 2022 00:02:42 -0700</lastBuildDate><atom:link href="https://datasciencela.github.io/tag/xgboost/index.xml" rel="self" type="application/rss+xml"/><item><title>XGBoost: meetup announcement</title><link>https://datasciencela.github.io/xgboost-a-scalable-tree-boosting-system/</link><pubDate>Thu, 19 May 2016 10:06:17 -0700</pubDate><guid>https://datasciencela.github.io/xgboost-a-scalable-tree-boosting-system/</guid><description>&lt;p&gt;&lt;strong&gt;XGBoost&lt;/strong&gt; is a fantastic open source implementation of Gradient Boosting Machines, one of the most accurate general purpose supervised learning algorithms. With interfaces from both R and Python among others, it has become a favorite tool in Kaggle competitions, and it&amp;rsquo;s also widely used in real-world applications. In this meetup we&amp;rsquo;ll have a talk from the best person on the topic,&lt;strong&gt;Tianqi Chen, main author of XGBoost.&lt;/strong&gt;&lt;/p&gt;</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://datasciencela.github.io/xgboost-a-scalable-tree-boosting-system/feature.png"/></item></channel></rss>