The document discusses decision trees, which are graphical representations of possible solutions to a decision based on certain conditions. It describes the basic components of decision trees, including decision nodes, chance nodes, and end nodes. It also covers different types of decision trees, algorithms for building decision trees like CART and C5.0, advantages like interpretability, and limitations like potential for overfitting. Finally, it provides examples of applications of decision trees in domains like business, healthcare, and engineering.

1. Decision Tree
Group Member:
Md Ibrahim Adham Mesu
Sakib Hasan Rial
Lifonar Afrin Lifa
Md Islam Khan
Abdullah Al Redwan

2. Content:
Introduction
Types
Algorithm
Cart Algorithm
C5.0 Algorithm
Advantages
Limitation
Application

3. What is Decision Tree?

4. Decision Tree:
A decision tree is a graphical representation of possible solutions to a
decision based on certain conditions.
It's called a decision tree because it starts with a single box (or root),
which then branches off into a number of solutions, just like a tree.
Decision trees provide an effective method of Decision Making.

5. Decision Tree:
A decision tree consists of 3 types of nodes
1. Decision nodes - commonly represented
by squares
2. Chance nodes - represented by circles
3. End nodes - represented by triangles
A decision tree consists of 2 types of
branches :
1. Decision branches
2. Event branches

6. Fig: Decision Tree

7. Algorithm
Input:
D // Training data
Output:
T //Decision tree
DT Build algorithm
// Simplistic algorithm to illustrate naive approach to building DT

8. Types of Decision Trees:
Types of decision tree is based on the type of target variable we
have. It can be of two types:
Categorical Variable Decision Tree
Continuous Variable Decision Tree

9. Categorical Variable Decision Tree:
Categorical Variable Decision Tree:
Decision Tree which has categorical
target variable then it called as
categorical variable decision tree.
E.g.:- In scenario, where the target
variable was “Student will distributed
in gender and height” i.e. YES or NO.

10. Continuous Variable Decision Tree:
Continuous Variable Decision Tree:
Decision Tree has continuous target variable then it is called as
Continuous Variable Decision Tree.
E.g.:-
A worker thinking about his salary and take decision. Is he accept
new job offer(yes/ no). In this case, we are predicting values for
continuous variable.

11. Continuous Variable Decision Tree:

12. Assumptions while creating Decision Tree:
Some of the assumptions we make while using Decision tree:
At the beginning, the whole training set is considered as the root.
Feature values are preferred to be categorical. If the values are
continuous then they are discretized prior to building the model.
Records are distributed recursively on the basis of attribute values.
Order to placing attributes as root or internal node of the tree is done
by using some statistical approach.

13. Different types of Algorithm:
HUNTS
CART
ID3
C5.0
SPRINT
CHAID

14. Cart Decision Tree:
sprit
Gini Index
Cost complexity pruning

15. C5.0 Decision Tree:
Multi sprit
Info gain
Pessimistic pruning

16. Advantages:
Decision Trees are easy to explain. It results in a set of rules.
It follows the same approach as humans generally follow while
making decisions.
Interpretation of a complex Decision Tree model can be simplified by
its visualizations. Even a naive person can understand logic.
The Number of hyper-parameters to be tuned is almost null.
It can be combined with other decision techniques.

17. Limitation:
There is a high probability of overfitting in Decision Tree.
Generally, it gives low prediction accuracy for a dataset as compared
to other machine learning algorithms.
Information gain in a decision tree with categorical variables gives a
biased response for attributes with greater no. of categories.
Calculations can become complex when there are many class labels.

18. Application of decision Tree:
Decision tree has been used to develop models for prediction and
classification in different domains some of which are:
Business management
Customer relationship management
Fraudulent statement detection
Engineering
Energy consumption
Fault diagnosis
Healthcare management
Agriculture

19. Applications in Real Life
Selecting a flight to travel
Choosing a best friend
Handling late night cravings

20. Applications in Business
Alternatives: Decision tree helps organizations to view the
alternatives way that can happen.
Events: Possible result may have two events.
Outcomes: Possible result from the decision tree.

21. References:
Kamiński, B.; Jakubczyk, M.; Szufel, P. (2017). "A framework for
sensitivity analysis of decision trees“
Quinlan, J. R. (1987). "Simplifying decision trees". International
Journal of Man-Machine Studies.
Venky Rao(2013 ).Journal “Introduction to Classification & Regression
Trees (CART).
Utgoff, P. E. (1989). “Incremental induction of decision trees”.
Machine learning.