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Predictive Data Mining: A Survey of Regression Methods
Abstract
Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. The idea is to build computer programs that sift through databases automatically, seeking regularities or patterns. Strong patterns, if found, will likely generalize to make accurate predictions on future data. Machine learning (ML) provides the technical basis of data mining. It is used to extract information from the raw data in databases—information that is expressed in a comprehensible form and can be used for a variety of purposes. Every instance in any data set used by ML algorithms is represented using the same set of features. The features may be continuous, categorical, or binary. If instances are given with known labels (the corresponding correct outputs), then the learning is called supervised in contrast to unsupervised learning, where instances are unlabeled (Kotsiantis & Pintelas, 2004). This work is concerned with regression problems in which the output of instances admits real values instead of discrete values in classification problems.
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