Explain Process of Classification with figure.

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 Process of Classification

Classification can be described by a two-step process given in appended block diagram:

Step 1

Also known as “supervised learning” as class labels are known. It is different than “Unsupervised learning” or clustering where class labels are not known. A model is built describing a predetermined set of data classes or concepts. The model is constructed by analyzing database tuples described by their attributes. Each tuple is assumed to belong to a predefined class and called as a class label attribute. Data tuples are also referred as Samples, Examples or Objects. Data tuples selected randomly form a training data set and are called training samples. The learning of the model is termed as” Supervised “ as it is told which class the training sample belongs to. This is in contrast to Clustering which is termed unsupervised learning.


Step 2



Test data verifies the accuracy of Classification Rules The model is used for classification. First the predictive accuracy of the model is estimated. If the accuracy is acceptable the model can be used to classify future tuples or objects for which class label is not known.




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