Data Classification — Definition & Difference from Prediction

What is Data Classification?

Data classification is a two-step, supervised process:

1. Learning / model construction step: a classification algorithm analyses a labelled training set (tuples whose class labels are known) and builds a model, e.g., a decision tree, a set of classification rules, a neural network, or an SVM.
2. Classification step: the learned model is applied to new, unseen tuples, assigning each to one of the predefined discrete class labels.

Its output is a categorical label (e.g., low-risk/high-risk, spam/not-spam), and its quality is assessed by accuracy (percentage of correctly classified test tuples).

Classification vs Prediction

Aspect	Classification	Prediction (regression)
Output	Discrete / categorical class labels	Continuous / numeric values
Nature	Predicts class of an object	Predicts a numerical value of an attribute
Evaluation	Accuracy / error rate (count of correct labels)	Mean squared error, mean absolute error, correlation
Examples	Predicting "yes/no", "spam/not-spam", loan rating	Predicting house price, sales figure, temperature
Techniques	Decision tree, rule-based, Bayesian, neural, SVM	Linear/ non-linear regression, neural net regression

Important nuance: In a broad sense, "prediction" covers both numeric prediction (regression) and classification — classification is a special case of prediction when the predicted value is a categorical label. In textbook terminology (Han & Kamber), classification predicts categorical labels while numeric prediction (regression) predicts continuous values; the two together form the "classification and prediction" task that examines both discrete & numeric outputs.