Volume 2, Issue 2

Study of Various Classification Algorithms using Data Mining


Smruti Ranjan Swain* and Smruti Smaraki Sarangi



Classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. An example would be assigning a given email into "spam" or "nonspam" classes or assigning a diagnosis to a given patient as described by observed characteristics of the patient (gender, blood pressure, presence or absence of certain symptoms, etc.).In the terminology of machine learning,[1] classification is considered an instance of supervised learning, i.e. learning where a training set of correctly identified observations is available. The corresponding unsupervised procedure is known as clustering, and involves grouping data into categories based on some measure of inherent similarity or distance. An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. The term "classifier" sometimes also refers to the mathematical function, implemented by a classification algorithm that maps input data to a category.



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Smruti Ranjan Swain* and Smruti Smaraki Sarangi | Study of Various Classification Algorithms using Data Mining | DOI : https://doi.org/10.62226/ijarst20130274

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