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Feel free to add/update and then do a pull request. (We build the binaries for 64-bit Linux and Windows. The file name will be of the form, where os is either linux or win64. Download the binary package from the Releases page. The content of the wiki is available as repository on GitHub. With this binary, you will be able to use the GPU algorithm without building XGBoost from the source. The wiki is based on Markdown articles, which are turned into static HTML using MkDocs (see here for details on writing articles). Visit the Weka Download page and locate a version of Weka suitable for your computer (Windows, Mac or Linux). It is a big 118 MB file that will take some minutes. Downloading of the executable file will start shortly. Step 2: It will redirect to a new webpage, click on Start Download.
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#INSTALL WEKA ON WINDOWS INSTALL#
Have a look at the Packages article for more information on this topic. Installing Weka on Windows: Follow the below steps to install Weka on Windows: Step 1: Visit this website using any web browser. Package manager interface, either official ones or unofficial ones. The steps for installing on Mac are as follows Download the Mac installation file. You just need to follow the instructions on this page to install WEKA for your OS.
#INSTALL WEKA ON WINDOWS MAC OS X#
WEKA supports installation on Windows, Mac OS X and Linux. With Weka 3.7.2 and later, you can easily install packages through Weka's To install WEKA on your machine, visit WEKA’s official website and download the installation file. Questions are mainly answered by volunteers, Weka users just like you. In mind that you cannot expect an immediate answer to your question(s). You can post questions to the Weka mailing list. Weka is a software available for Windows platforms that are specifically purposed for data. Have a look at the Frequently Asked Questions (FAQ), the Troubleshooting article or search the mailing list archives.ĭon't forget to check out the documentation and the online courses. Weka is used for data mining purposes by utilizing a wide assortment of machine learning algorithms.