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Let us understand about decision trees with the help of an example, where you need to identify the person is male
or female based on the height, weight, and foot size.
height > 5.9
Yes No
Male weight <= 150
No Yes
foot size >= 10 Female
Yes No
Male Female
Following are some of the important points to consider while designing a decision tree:
• There can be a possibility of multiple decision trees that lead to correct prediction for a single dataset. The
simplest one should be chosen.
• The dataset might contain redundant data at times, which does not have any reference while creating a decision
tree. Therefore, only those parameters that affect the output directly must be included.
• While making decision trees, one should take a look at the dataset given to them and try to figure out what
pattern the output leaf follows. Try selecting any one output and on its basis, find out the common links that all
the similar outputs have.
Task #Experiential Learning
1. The following is a dataset comprising 4 parameters which lead to the prediction of a student choosing
a Science stream in senior secondary school. Make a decision tree for this dataset:
Aptitude Logical Skills Analytical Skills Reasoning Stream Selection: Science
High High High High Yes
High Low Low Low No
Low High High High Yes
Low Low Low High No
Low High High Low No
2. Make a decision tree to choose a book from the library based on the following selections.
Choice 1: Fiction—Sci-Fi, Mystery, Classics.
Choice 2: No Fiction—Travel Diaries, Autobiographies, History, Travelogues.
3. Using the given data, create a Decision Tree to help spot the elephant! Classify the animals based on
the following characteristics:
Choice 1: Large size and long trunk.
Choice 2: Not large size and no trunk.
Collaborate in your groups and present your Decision Trees!
AI Reflection, Project Cycle and Ethics 209

