![]() This is a new function in Weka from version 3.7.x (version for Developers). Classifier output can be compared to training data in order to detect outliers and observe classifier characteristics and decision boundaries. For specific methods, there are specialized tools for visualization, such as a tree viewer for any method that produces classification trees, a Bayes network viewer with automatic layout, and a dendrogram viewer for hierarchical clustering Time Series Forecasting Data Visualizationĭata can be inspected visually by plotting attribute values against the class, or against other attribute values. The set of attributes used is essential for classification performance. Various selection criteria and search methods are available. Weka has most classic algorithms for clustering such as: Simple KMeans, Hierarchical class clustering, simple expectation maximization (EM). Classifiers can be divided into “Bayesian” methods (Naive Bayes, Bayesian nets etc.), lazy methods (nearest neighbor and variants), rule-based methods (decision tables, OneR, RIPPER), tree learners (C4.5, Naive Bayes trees, M5, J.48 etc), function-based learners (linear regression, SVMs, Multilayer Perceptron, Gaussian processes) and miscellaneous methods. Weka has a lot of classification methods. For data processing, Weka has over 75 methods for filtering, ranging from basic to advanced operators eg principal component analysis. It also supports most common database management systems (DBMS) including HSQL, SQL SERVER, MySQL, PostgreSQL etc through java connections. Weka supports various file formats e.g, CSV, Matlab etc and its own file format (ARFF). All of Weka’s techniques are predicated on the assumption that the data is available as a single flat file or relation, where each data point is described by a fixed number of attributes. Here are some main features of Weka: Data Preprocessing Weka supports several standard data mining tasks with many standard data mining algorithms ranging from normal ones to really complex ones. Also see our follow up post on Intro Primer To WEKA Explorer For Machine Learning Why Weka? ![]() RobustTechHouse is a web
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