Explain classification with examples.

Classification

Classification is the process of identifying the category or class label of the new observation to which it belongs. For this, Classification extracts the classification model (classifier) from the given training set and then uses this model to decide the category of new data tuple to be classified. For example, before starting your seventh semester Project, you want to predict whether it is accepted or rejected in the final project defense. In this case, a classification model is required to predict class labels such as 'accepted' or 'rejected. For this purpose, you learn the classification model by consulting the previously passed and failed projects (training dataset) and then you decide whether your project will be accepted or rejected.

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 Classification

• Classification is the process of finding a model (or function) that describes and distinguishes data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. The derived model is based on the analysis of a set of training data (i.e., data objects whose class label is known).

• “How is the derived model presented?” The derived model may be represented in various forms, such as classification (IF-THEN) rules, decision trees, mathematical formulae, or neural networks.

• Example:-