## The Fundamentals of Classification

While regression focuses on creating a model that best fits our data to predict the future, classification is all about creating a model that separates our data into separate classes.

Assuming that you have some data belonging to separate classes, classification helps you predict the class a new data point belongs to. A classifier is a model that determines the label value belonging to any data point in the domain. Suppose you have a set of points, **P = {p1, p2, p3, ..., pm}**, and another set of points, **Q = {q1, q2, q3, ..., qn}**. You treat these points as members of different classes. For simplicity, we could imagine that **P** contains credit-worthy individuals, and **Q** contains individuals that are risky in terms of their credit repayment tendencies.

You can divide the state space so that all points in **P** are on one cluster of the state space, and then disjoint from the state space cluster containing all points in **Q**. Once you find these bounded spaces, called **clusters...**