Classification Learning

• Supervised Learning,inductive learning
  Inductive learning is the technique to infer information that is generalised from the database.It is a higher level information in that is a general statement about objects in the database
• Examples are described in terms of attributes(also called features, variables,dimensions). An attribute's range can be any one of:
• Categorical (range is unordered set of symbolic values,possibly partially ordered)
  • Ordinal (range is linearly totally ordered set of symbolic values

  • Numeric or continuous(a dense subset of the reals)
• Class(output variable):categorical values.If the class variable is numeric,then the problem becomes a regression problem.This is an important distinction as we shall see later.
• Input is in form of feature vectors.If problem has k features then training data consists of k-feature vectors.A class is given for each example (class vector of data set)