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technobium | Technology blog
www.decebalus.de/
Understand how a decision tree learning work. The article makes use of Java and Weka to help explain the basic concepts.

Decebalus.de

Conversion Technology Metas Server
Decebalus.de is hosted in Germany . Decebalus.de doesn't use HTTPS protocol. Number of used technologies: 7. First technologies: CSS, Google Font API, Html, Number of used javascripts: 6. First javascripts: K88vSikoSi0u1i8...93Mw8AA.js, M9RPLkqszM_TLa7...AI9IBMA.js, M9RPTiwoSc5I1M8...nQjQAAA.js, Number of used analytics tools: 1. First analytics tools: Google Analytics, Its server type is: Apache/2.2.22 (Ubuntu). Its CMS is: Wordpress.

Technologies in use by Decebalus.de

Technology

Number of occurences: 7
  • CSS
  • Google Font API
  • Html
  • Html5
  • Javascript
  • Php
  • Pingback

Javascripts

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Content Management System

Number of occurences: 1
  • Wordpress

Analytics

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  • Google Analytics

Advertise

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  • Google Adsense

Server Type

  • Apache/2.2.22 (Ubuntu)

Powered by

  • PHP/5.3.10-1ubuntu3.11

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HTTPS (SSL) - Decebalus.de

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    Meta - Decebalus.de

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      Content: Understand how a decision tree learning work. The article makes use of Java and Weka to help explain the basic concepts.
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      Content: decision trees,id3,java,weka,machine learning,apache spark,logistic regression,scala,artificial neural networks,neuroph,backpropagation,apache opennlp,natural language processing,mahout,sentiment analysis,naive bayes,classification,cognitive computing,ibm watson,ibm bluemix,web,servlet,cogninitive computing,apache mahout,tutorial,fraud detection,linear regression,jfreechart,object detection,opencv,computer vision,clustering using
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      Content: technobium
    • Name: twitter:description
      Content: Decision trees are a classic supervised learning algorithms, easy to understand and easy to use. In this article we will describe the basic mechanism behind decision trees and we will see the algorithm into action by using Weka (Waikato Environment for Knowledge Analysis). Decision trees The main concept behind decision tree learning is the following: starting from the training data, we will build a predictive model which is mapped to a tree structure. The goal is to achieve perfect classification with minimal number of decision, although not always possible due to noise or inconsistencies in data. Here is a sample training data set - we are trying to predict the success of movie taking into consideration features like: country of origin, if big stars are playing and the movie's genre. From this table we need a decision tree which can be used to predict the success or failure of a new film. A decision tree learning algorithm will return a predictive model which could be graphically
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      Content: http://technobium.com/wordpress/wp-content/uploads/2014/11/logo_2501.png
    • Name: generator
      Content: WordPress 4.5.3

    Server / Hosting

    • IP: 91.194.90.40
    • Latitude: 51.30
    • Longitude: 9.49
    • Country: Germany

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    HTTP Header Response

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