<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>GBM on DataScience.LA</title><link>https://datasciencela.github.io/tag/gbm/</link><description>Recent content in GBM 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/gbm/index.xml" rel="self" type="application/rss+xml"/><item><title>XGBoost workshop and meetup talk with Tianqi Chen</title><link>https://datasciencela.github.io/xgboost-workshop-and-meetup-talk-with-tianqi-chen/</link><pubDate>Mon, 06 Jun 2016 09:00:50 -0700</pubDate><guid>https://datasciencela.github.io/xgboost-workshop-and-meetup-talk-with-tianqi-chen/</guid><description>&lt;p&gt;&lt;a href="https://github.com/dmlc/xgboost"&gt;XGBoost&lt;/a&gt; is a fantastic open source implementation of &lt;a href="https://en.wikipedia.org/wiki/Gradient_boosting"&gt;Gradient Boosting Machines&lt;/a&gt;, a general purpose supervised learning method that achieves the highest accuracy on a wide range of datasets in practical applications. Deep learning is all the hype now, but apart from specific domains such as images, speech or text (i.e. problems with higher-level abstractions to be learnt and/or perception problems, where deep learning achieved some remarkable results indeed), it is usually outperformed by Gradient Boosting in a majority of general business domains and supervised learning applications. Proof of this and also because XGBoost has an easy-to-use interface from both &lt;a href="http://dmlc.ml/rstats/2016/03/10/xgboost.html"&gt;R&lt;/a&gt; and Python, XGBoost has become a favorite tool in Kaggle competitions. Besides feature engineering, cross-validation and ensembling, XGBoost is a key ingredient for achieving the highest accuracy in many data science competitions and more importantly in practical applications.&lt;/p&gt;</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://datasciencela.github.io/xgboost-workshop-and-meetup-talk-with-tianqi-chen/feature.png"/></item></channel></rss>