This document discusses machine learning concepts like supervised and unsupervised learning. It explains that supervised learning uses known inputs and outputs to learn rules while unsupervised learning deals with unknown inputs and outputs. Classification and regression are described as types of supervised learning problems. Classification involves categorizing data into classes while regression predicts continuous, real-valued outputs. Examples of classification and regression problems are provided. Classification models like heuristic, separation, regression and probabilistic models are also mentioned. The document encourages learning more about classification algorithms in upcoming videos.

1. OMega TechEd
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2. BUSINESS INTELLIGENCE
CLASSIFICATION & REGRESSION
Mrs. Megha Sharma
M.Sc. Computer Science. B.Ed.

3. Learning?
“Learning in a computer system means acquiring information and
storing it for future reference which can give out knowledge.”
When we can make a machine learn like human or rather process and
gather information and store for further processing we call it as
“Machine Learning”
Machine learning is a subset of Artificial intelligence which defines
how machines learns without being programmed or with less
programming.

4. Machine LEARNING
Machine Learning
Supervised
Learning
Classification Regression
Unsupervised
Learning
Clustering

5. Supervised and Unsupervised Learning
Supervised learning: Known inputs and outputs in the system and
system acts on it with the given set of rules.
Unsupervised learning: unknown inputs and outputs are not fixed.
Green
Red

6. CLASSIFICATION
Classification is a process of categorizing a given set of data into
classes, It can be performed on both structured or unstructured data.
The process starts with predicting the class of given data points. The
classes are often referred to as target, label or categories.
Class A
Class B

7. REGRESSION
A technique for determining the statistical relationship between two or
more variables where a change in a dependent variable is associated
with, and depends on, a change in one or more independent variables.
A regression problem is used when the output variable is a real or
continuous value, such as “salary” or “weight”.

8. Comparison
Classification
 Prediction are made by classifying
data into different categories.
 The output variable in classification
is categorical (or discrete)
 E.g.(i)Predicting gender of a person.
(ii) Predicting result of a student
(pass or fail)
Regression
 The system attempt to predict
value based on past data.
 The output variable in regression is
numerical (or continuous).
 E.g.(i) Predicting age of a person.
(ii) Predicting percentage of a
student.

9. CLASSIFICATION MODELS.
Heuristic models
Separation models
Regression models
Probabilistic models

10. Characteristics of Classification models.

11. Thanks For Watching.
Next Topic : Classification Algorithm.

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