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<title>Weka 3 - Data Mining with Open Source Machine Learning Software in Java </title>
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<li><a href="./index.html">Weka</a></li>
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<h1><strong>WEKA</strong></h1>
<H2>The workbench for machine learning</h2>
</header>
<p>Weka is tried and tested open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as <a href="https://markahall.blogspot.co.nz/2015/06/cpython-integration-in-weka.html" target="_blank">scikit-learn</a>, <a href="https://markahall.blogspot.com/2012/07/r-integration-in-weka.html" target="_blank">R</a>, and <a href="https://deeplearning.cms.waikato.ac.nz" target="_blank">Deeplearning4j</a>.

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<a href="https://waikato.github.io/weka-wiki/downloading_weka/" class="button">Download and install</a>
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<h1 name="GetStart" id="GetStart">Getting Started</h1>
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<p>Video from <a href="https://twitter.com/random_forests">Josh Gordon</a>, Developer Advocate for @GoogleAI.</p>
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<h2 name="learn" id ="learn" >Machine Learning without Programming</h2>
<p>Weka can be used to build machine learning pipelines, train classifiers, and run evaluations without having to write a single line of code:</p>

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<span class="image fit"><img src="https://waikato.github.io/weka-site/images/Explorer-1.png" alt=""/></span>
<h3>Open a dataset</h3>
<p>First, we open the <a href="https://waikato.github.io/weka-wiki/datasets/" target="_blank">dataset</a> that we would like to evaluate.</p>
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<span class="image fit"><img src="https://waikato.github.io/weka-site/images/Explorer-2.png" alt=""/></span>
<h3>Choose a classifier</h3>
<p> Second, we select a learning algorithm to use, e.g., the J48 classifier, which learns decision trees.</p>
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<span class="image fit"><img src="https://waikato.github.io/weka-site/images/Explorer-3.png" alt="" /></span>
<h3>Evaluate predictive accuracy</h3>
<p>Finally, we run a 10-fold cross-validation evaluation and obtain an estimate of predictive performance.</p>
</article>
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<p>Note that programmers can also easily implement this pipeline using Weka's Java API:</p>
<p>
<iframe
src="https://carbon.now.sh/embed/?bg=rgba(171%2C%20184%2C%20195%2C%201)&t=seti&wt=sharp&l=text%2Fx-java&ds=true&dsyoff=20px&dsblur=68px&wc=true&wa=true&pv=0px&ph=0px&ln=false&fm=Hack&fs=18px&lh=139%25&si=false&es=4x&wm=false&code=DataSource%2520source%2520%253D%2520new%2520DataSource(%2522%252Fsome%252Fwhere%252Fdata.arff%2522)%253B%250AInstances%2520data%2520%253D%2520source.getDataSet()%253B%250A%250AJ48%2520tree%2520%253D%2520new%2520J48()%253B%250AEvaluation%2520eval%2520%253D%2520new%2520Evaluation(data)%253B%250Aeval.crossValidateModel(tree%252C%2520data%252C%252010%252C%2520new%2520Random(1))%253B"
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<span><img name="deep learning" alt="Deep Learning with Weka" width="100%" src="https://waikato.github.io/weka-site/images/deeplearning.jpg" /></span>
</div>
<div class="col-8 col-7-large col-12-medium">
<header>
<h1>Deep Learning with WEKA </h1>
</header>
<p><a href="https://deeplearning.cms.waikato.ac.nz">WekaDeeplearning4j</a> is a deep learning package for Weka. Deep neural networks, including convolutional networks and recurrent networks, can be trained directly from Weka's graphical user interfaces, providing state-of-the-art methods for tasks such as image and text classification.</p>


<a href="https://deeplearning.cms.waikato.ac.nz" class="button">WekaDeeplearning4j</a>

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<h2>WEKA Interoperability</h2>
<p>WEKA can be integrated with the most popular data science tools.</p>
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<a href="#" class="image featured"><img src="https://waikato.github.io/weka-site/images/R.png" height="200" alt="" /></a>
<h3>R</h3>
<p>Weka models can be used, built, and evaluated in <a href="https://www.r-project.org/" target="_blank">R</a> by using the <a href="https://www.rdocumentation.org/packages/RWeka/" target="_blank">RWeka</a> package for R; conversely, R algorithms and visualization tools can be invoked from Weka using the <a href="https://markahall.blogspot.co.nz/2012/07/r-integration-in-weka.html" target="_blank">RPlugin</a> package for Weka.</p>
</section>
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<section class="box style1">
<a href="#" class="image featured"><img src="https://waikato.github.io/weka-site/images/python.png" height="100" alt="" /></a>
<h3>Python</h3>
<p>Weka's functionality can be accessed from Python using the <a href="https://github.com/fracpete/python-weka-wrapper3" target="_blank">Python Weka Wrapper</a>. Conversely, Python toolkits such as scikit-learn <a href="https://markahall.blogspot.co.nz/2015/06/cpython-integration-in-weka.html" target="_blank">can be used from Weka</a>.</p>
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<a href="#" class="image featured"><img src="https://waikato.github.io/weka-site/images/spark.png" height="150" alt="" /></a>
<h3>Spark</h3>
<p>For running Weka-based algorithms on truly large datasets, the <a href="https://markahall.blogspot.com/2015/03/weka-and-spark.html" target="_blank">distributed Weka for Spark
package</a> is available. It makes it possible to train
any Weka classifier in Spark, for example. </p>
</section>
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Weka is proudly brought to you by the <a href="https://www.cs.waikato.ac.nz/ml/index.html" target="_blank">Machine Learning Group</a> at the <a href="https://waikato.ac.nz" target="_blank">University of Waikato.
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