1. Prepared by: Mahmoud Rafeek Al-Farra
College of Science & Technology
Dep. Of Computer Science & IT
BCs of Information Technology
Data Mining
Chapter 4_2: Classification Methods
(Examples)
Prepared by: Mahmoud Rafeek Al-Farra
2013

2. Course’s Out Lines Introduction Data Preparation and Preprocessing
Data Representation
Classification Methods
Evaluation
Clustering Methods
Mid Exam
Association Rules
Knowledge Representation
Special Case study : Document clustering
Discussion of Case studies by students

3. Out Lines Comparing Classification Methods Machine learning techniques
Decision Trees
k-Nearest Neighbors
Naïve Bayesian Classifiers
Neural Networks

4. Comparing Classification Methods
Predictive Accuracy: Ability to correctly predict the class label.
Speed: Computation costs involved in generating and using model
Robustness: Ability to make correct predictions given noisy or/and missing values

5. Comparing Classification Methods
Scalability: Ability to construct model efficiently given large amounts of data
Interpretability: Level of understanding and insight that is provided by the model.

6. Machine learning techniques
Things learn when they change their behavior in a way that makes them perform better in the future.
Machine learning
is the subfield of artificial intelligence that is concerned with the design and development of algorithms that allow computers (machines) to improve their performance over time (to learn) based on data, such as from sensor data or databases

7. Machine learning techniques
Examples of machine learning techniques:
Decision Trees
k-Nearest Neighbors
Naïve Bayesian Classifiers

8. Decision Trees
Decision tree learning is a common method used in data mining.
It is an efficient method for producing classifiers from data.
A Decision Tree is a tree-structured plan of a set of attributes to test in order to predict the output.

9. Decision Trees

10. Decision tree consist of:
An internal node is a test on an attribute, e.g. Body temperature .
A branch represents an outcome of the test, e.g., Warm
A leaf node represents a class label e.g. Mammals
At each node, one attribute is chosen to split training examples into distinct classes as much as possible
A new case is classified by following a matching path to a leaf node.

11. Decision tree consist of:

12. Weather Data: Play or not Play?
Outlook
Temperature
Humidity
Windy
Play?
sunny
hot
high
false No
High
true
overcast
Yes
rain
mild
cool
normal

13. Weather Data: Play or not Play?
Outlook
Case Study: How To Build a tree?
sunny
rain
overcast
Humidity
Yes
Windy
true
false
high
normal No
Yes No
Yes

14. How To Build a tree? Top-down tree construction
Which is the best attribute? ….

15. Next …
k-Nearest Neighbors
Naïve Bayesian Classifiers

16. Thanks