Basic concepts DatasetĪ set of data items, the dataset, is a very basic concept of machine learning. If you want to know exactly what is going on, take a look at the source code, which can be found in weka-src.jar and can be extracted via the jar utility from the Java Development Kit. Prepare to use it since this introduction is not intended to be complete. Note that, in the doc directory of the WEKA installation directory, you can find documentation of all Java classes in WEKA. Afterwards, some practical examples are given. Following that, we will consider some machine learning algorithms that generate classification models. Then, we will describe the weka.filters package, which is used to transform input data, e.g., for preprocessing, transformation, feature generation and so on. We will begin by describing basic concepts and ideas. This document serves as a brief introduction to using WEKA from the command line interface. Regression, association rule mining, time series prediction, and clustering algorithms have also been implemented. Its main strengths lie in the classification area, where many of the main machine learning approaches have been implemented within a clean, object-oriented Java class hierarchy. WEKA is a comprehensive workbench for machine learning and data mining.
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