OBJECTIVES

  1. Like human learning from past experiences, a computer does not have “experiences”.
        1. A computer system learns from data, which represent some “past experiences” of an application domain.
  2. Our focus: learn a target function that can be used to predict the values of a discrete class attribute, e.g., approve or not-approved, and high-risk or low risk.
  3. The task is commonly called: Supervised learning, classification, or inductive learning.
  4. Data: A set of data records (also called examples, instances or cases) described by
    1. k attributes: A1, A2, … Ak.
    2. a class: Each example is labelled with a pre-defined class.
  5. Goal: To learn a classification model from the data that can be used to predict the classes of new (future, or test) cases/instances.

 

classification and prediction by v. vanthana