Download 5.2 General Approach to Solving a Classification Problem

5.1 Basics
The input data for a classification task is a collection of records. Each record, also known as an
instance or example, is characterized by a tuple (x, y), where x is the attribute set and y is a special
attribute, designated as the class label. Sample data set used for classifying vertebrates into one of
the following categories: mammal, bird, fish, reptile, or amphibian. The attribute set includes
properties of a vertebrate such as its body temperature, skin cover, method of reproduction ability
to fly, and ability to live in water. The attribute set can also contain continuous features. The class
label, on the other hand, must be a discrete attribute. This is a key characteristic that distinguishes
classification from regression, a predictive modelling task in which y is a continuous attribute.
Definition 3.1 (Classification). Classification is the task of learning a target function f that maps
each attribute set x to one of the predefined class labels y. The target function is also known
informally as a classification
Descriptive Modeling
model.A classification model is useful for the following purposes.
A classification model can serve as an explanatory tool to distinguish between objects of different
Classes. For example, it would be useful for both biologists and others to have a descriptive model.
Predictive Modeling
A classification model can also be used to predict the class label of unknown records. As a
classification model can be treated as a black box that automatically assigns a class label when
presented with the attribute set of an unknown record. Suppose we are given the following
characteristics of a creature known as a gila monster: Classification techniques are most suited for
Predicting or describing data sets with binary or nominal categories. They are less effective for
ordinal categories (e.g., to classify a person as a member of high-, medium-, or low-income group)
because they do not consider the implicit order among the categories. Other forms of relationships,
such as the subclass–super class relationships among categories
5.2 General Approach to Solving a Classification Problem
A classification technique (or classifier) is a systematic approach to building classification models
from an input data set. Examples include decision tree classifiers, rule-based classifiers, neural
networks, support vector machines and naive Bayes classifiers. Each technique employs a learning
algorithm to identify a model that best fits the relationship between the attribute set and class label
of the input data. The model generated by a learning algorithm should both fit the input data well
and correctly predict the class labels of records it has never seen before. Therefore, a ey objective
of the learning algorithm is to build models with good generalization capability; i.e., models that
accurately
predict
the
class
labels
of
previously
unknown
records.
Figure 5.1 General approach for building a classification model.
Figure 5.1 shows a general approach for solving classification problems. First, a training set
consisting of records whose class labels are known must be provided. The training set is used to
build a classification model, which is subsequently applied to the test set,
records with unknown class labels.
which consists of