In Learning, a model is used to analyze a training data set is composed of training data
samples that are randomly selected from a sample population.
In Classification, the model is used for estimation using class-labelled, randomly selected
test samples. Classification is usually associated with finding a known data class for the given
unknown data, which is analogous to labelling the unlabelled data. "Classification is the
process that finds the common properties along a set of objects in a data set and classifies
them into different classes according to classification model."
