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)
The derived model may be represented in various forms, such as classification (IF-THEN) rules, decision trees, mathematical formulae, or neural networks.
Whereas classification predicts ‘categorical ;(discrete, unordered) labels, .prediction models continuous-valued functions. That is, it is used to predict missing or unavailable numerical data values rather than class labels. Although .1 the term prediction may refer to both numeric prediction and class label prediction. Prediction also encompasses the identification of distribution trends based on available data.
Classification and prediction may need to be preceded by relevance analysis, which attempts-to identifies attributes that. do not contribute to the classification or prediction process. These attributes ‘can then be excluded.