![]() MEKA is based on the WEKA Machine Learning Toolkit. This different from the ‘standard’ case which involves only a single target variable. In multi-label classification, we want to predict multiple output variables for each input instance. The MEKA project provides an open source implementation of methods for multi-label learning and evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems. ![]() It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Massive Online Analysis (MOA) is a popular open source framework for data stream mining, with a very active growing community. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka has a collection of machine learning algorithms for data mining tasks.
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