It makes it possible to train any Weka classifier in Spark, for example.
Conversely, Python toolkits such as scikit-learn can be used from Weka.įor running Weka-based algorithms on truly large datasets, the distributed Weka for Spark package is available. weka wekasoftwareDownload and Install Weka Software in Windows 10 Download. Weka's functionality can be accessed from Python using the Python Weka Wrapper. Weka models can be used, built, and evaluated in R by using the RWeka package for R conversely, R algorithms and visualization tools can be invoked from Weka using the RPlugin package for Weka. WEKA can be integrated with the most popular data science tools. 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. WekaDeeplearning4j is a deep learning package for Weka. Note that programmers can also easily implement this pipeline using Weka's Java API: What WEKA offers is summarized in the following diagram. Second, we select a learning algorithm to use, e.g., the J48 classifier, which learns decision trees.įinally, we run a 10-fold cross-validation evaluation and obtain an estimate of predictive performance. WEKA - an open source software provides tools for data preprocessing, implementation of several Machine Learning algorithms, and visualization tools so that you can develop machine learning techniques and apply them to real-world data mining problems.
2) After successful download, open the file location and double click on the downloaded file. Weka can be used to build machine learning pipelines, train classifiers, and run evaluations without having to write a single line of code:įirst, we open the dataset that we would like to evaluate. Check the configuration of the computer system and download the stable version of WEKA (currently 3.8) from this page.
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 scikit-learn, R, and Deeplearning4j.