Weka is a machine learning workbench that provides a general-purpose environment for data mining tasks such as classification, regression, clustering, and feature selection, which are common in bioinformatics research. It includes a wide range of machine learning algorithms and data preprocessing methods, along with graphical user interfaces for data exploration and comparing different learning techniques on the same problem. Weka can process data in the form of a single relational table and aims to help users extract useful information from data and identify suitable algorithms for generating accurate predictive models. Weka has been used in bioinformatics for tasks such as automated protein annotation, probe selection for gene-expression arrays, cancer diagnosis, computational modeling of frame-shifting sites, plant genotype discrimination, gene expression profiling, and rule extraction. Many of its algorithms are described in Witten and Frank (2000). The system is designed to offer maximum flexibility when applying machine learning methods to new datasets, including algorithms for learning different types of models, feature selection schemes, and preprocessing methods. The main interface of Weka is the Explorer, which includes panels for data preprocessing, classification, clustering, association rule generation, and visualization. The Explorer allows users to perform tasks such as data preprocessing, classification, clustering, and visualization. It also provides tools for evaluating learning algorithms using cross-validation or a holdout set, and for visualizing classifier performance. Weka also includes an alternative graphical user interface called 'Knowledge Flow' and a third interface called 'Experimenter' for conducting experiments comparing the performance of multiple learning schemes on multiple datasets. Weka is implemented in Java and runs on almost any computing platform. The paper acknowledges the contributions of many individuals to the Weka project.Weka is a machine learning workbench that provides a general-purpose environment for data mining tasks such as classification, regression, clustering, and feature selection, which are common in bioinformatics research. It includes a wide range of machine learning algorithms and data preprocessing methods, along with graphical user interfaces for data exploration and comparing different learning techniques on the same problem. Weka can process data in the form of a single relational table and aims to help users extract useful information from data and identify suitable algorithms for generating accurate predictive models. Weka has been used in bioinformatics for tasks such as automated protein annotation, probe selection for gene-expression arrays, cancer diagnosis, computational modeling of frame-shifting sites, plant genotype discrimination, gene expression profiling, and rule extraction. Many of its algorithms are described in Witten and Frank (2000). The system is designed to offer maximum flexibility when applying machine learning methods to new datasets, including algorithms for learning different types of models, feature selection schemes, and preprocessing methods. The main interface of Weka is the Explorer, which includes panels for data preprocessing, classification, clustering, association rule generation, and visualization. The Explorer allows users to perform tasks such as data preprocessing, classification, clustering, and visualization. It also provides tools for evaluating learning algorithms using cross-validation or a holdout set, and for visualizing classifier performance. Weka also includes an alternative graphical user interface called 'Knowledge Flow' and a third interface called 'Experimenter' for conducting experiments comparing the performance of multiple learning schemes on multiple datasets. Weka is implemented in Java and runs on almost any computing platform. The paper acknowledges the contributions of many individuals to the Weka project.