For B.Tech, MCA students. Helps to prepare for University Examinations, Interview Questions.

1. Classification and Prediction

2. Classification
• Classification (category into which something is put)
• Classification models predicts categorical class labels.
• E.g. to categorize bank loan applications as “safe” or “risky”.
• Classification is also known as supervised learning.

3. How does classification work?
• Data classification is a two-step process:
(1) Building the classifier or model (learning step) based on
previous data.
(1) Using the classifier for classification (classification step)
based on new data.

4. Learning Step
• In the learning step, classifier is build using the classification
algorithm.
• The classifier is built from a training set made up of database
tuples and their associated class labels.
• The individual tuples making up the training set are called
training tuples and are also referred to as samples, examples,
instances, datapoints or objects.

5. Learning Step

6. Learning Step
• Training data are analyzed by a classification algorithm.
• In the previous example, class label is tenured and the learned
model or classifier is represented in the form of classification
rules or decision trees or mathematical formulae.

7. Classification Step

8. Classification Step
• Here test data is used to estimate the accuracy of
classification rules.
• If the accuracy is considered acceptable, the rules can be
applied to the classification of new data tuples (whose value
are not known)

9. Prediction
• Prediction models predict continuous-valued functions. (i.e.
predicts unknown or missing values)
• E.g. predict the expenditure in dollars of potential customers
on computer equipment, given their income and occupation.
• Regression Analysis is a statistical methodology that is most
often used for numeric prediction.

10. Difference between Classification and Prediction
• Classification • Prediction
• Predicts categorical
class labels (discrete
or nominal)
• E.g. if I give $ x loan to
a customer will it be
safe or risky.
• Given the income and
occupation of a
customer will he buy
computer or not.
• Models continuous-
valued functions. i.e.
predicts unknown or
missing values.
• E.g. how much loan
should I give to a
customer, to be safe.
• How much a person
spend on computer,
given his income and
occupation.

11. Difference between Classification and
Prediction