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Can select appropriate value of K based on cross-validation.
SimpleCart: Class implementing minimal cost-complexity pruning. RandomTree: Class for constructing a tree that considers K randomly chosen attributes at each node. RandomForest: Class for constructing a forest of random trees. Implements base routines for generating M5 Model trees and rules. HoeffdingTree: A Hoeffding tree (VFDT) is an incremental, anytime decision tree induction algorithm that is capable of learning from massive data streams, assuming that the distribution generating examples does not change over time. BFTree: Class for building a best-first decision tree classifier. ADTree: Class for generating an alternating decision tree. SMO: Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier. PaceRegression: Class for building pace regression linear models and using them for prediction. NonNegativeLogisticRegression: Class for learning a logistic regression model that has non-negative coefficients. MultilayerPerceptron: A Classifier that uses backpropagation to classify instances. LinearRegression: Class for using linear regression for prediction. GaussianProcesses: Implements Gaussian Processes for regression without hyperparameter-tuning. BayesNet: Bayes Network learning using various search algorithms and quality measures. BayesianLogisticRegression: Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors. Weka has a large number of regression and classification tools. A comprehensive collection of data preprocessing and modelling techniques. Cross-Platform support (Windows, Mac OS X and Linux). The Workbench provides an all-in-one application that subsumes all the major WEKA GUIs described above. 5- Workbench:Ī new user interface which is available from Weka 3.8.0. The KnowledgeFlow can handle data either incrementally or in batches (the Explorer handles batch data only). Supports essentially the same functions as the Explorer but with a drag-and-drop interface. Visualize: which enables you to view an interactive 2D plot of the data.Īn environment that enables the user to create, run, modify, and analyse experiments in a more convenient manner than is possible when processing the schemes individually 4- KnowledgeFlow:. Select attributes: to select the most relevant attributes in the data. Associate: to learn association rules for the data. Cluster: to learn clusters for the data. Classify: to train and test learning schemes that classify or perform regression. Preprocess: which enables you to choose and modify the data being acted on. 2- Explorer:Īn environment for exploring data with WEKA. It offers a simple Weka shell with separated commandline and output. Provides full access to all Weka classes, i.e., classifiers, filters, clusterers, etc., but without the hassle of the CLASSPATH.
Weka 3.8 and 3.9 feature a package management system that makes it easy for the Weka community to add new functionalities to Weka. Stable versions receive only bug fixes, while the development version receives new features.
For the bleeding edge, it is also possible to download nightly snapshots. There are two versions of Weka: Weka 3.8 is the latest stable version and Weka 3.9 is the development version. This tool doesn’t support processing of related charts however, there are many tools allowing combining separate charts into a single chart, which can be loaded right into Weka. Weka provides access to SQL databases using Java Database Connectivity (JDBC) and allows using the response for an SQL query as the source of data.
Weka is open source software released under the GNU General Public License. It is written in Java and developed at the University of Waikato, New Zealand. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization. WEKA (Waikato Environment for Knowledge Analysis) is a collection of machine learning algorithms for data mining tasks.
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