Decision Tree Algorithm

What is Decision Tree?

Decision Tree means a Decision Tree makes predictions using a tree-like structure of questions and decisions.

In real programs, this topic helps in rule-based decisions. Learn the idea first, then type the program yourself and compare the output.

Why should you learn this?

• It is useful for rule-based decisions.
• It connects with explainable classification.
• It improves your ability to read, write and debug Python programs.

Important Terms

These terms are used directly in this lesson. Understand them before memorising the code.

Syntax / Basic Pattern

The simple pattern is: prepare data, apply the concept, then show the result.

from sklearn.tree import DecisionTreeClassifier
X = [[30], [45], [60], [75], [90]]
y = ["Fail", "Fail", "Pass", "Pass", "Pass"]
model = DecisionTreeClassifier(random_state=0)
model.fit(X, y)
print(model.predict([[55]])[0])

Complete Example Program

from sklearn.tree import DecisionTreeClassifier

X = [[30], [45], [60], [75], [90]]
y = ["Fail", "Fail", "Pass", "Pass", "Pass"]

model = DecisionTreeClassifier(random_state=0)
model.fit(X, y)
print(model.predict([[55]])[0])

Expected Output

Program Explanation

• from sklearn.tree import DecisionTreeClassifier imports ready-made features from a module/library.
• X = [[30], [45], [60], [75], [90]] stores a value in X.
• y = ["Fail", "Fail", "Pass", "Pass", "Pass"] stores a value in y.
• model = DecisionTreeClassifier(random_state=0) stores a value in model.
• model.fit(X, y) performs the next step of the program logic.
• print(model.predict([[55]])[0]) displays information or calculated result on the screen.

Where will you use it?

• Rule-based decisions.
• Explainable classification.
• Visual model explanation.

Common Mistakes

• Training and testing the model on the same data.
• Using an algorithm without understanding the input features.
• Reporting only accuracy without checking actual mistakes and limitations.

Practice Tasks

1. Type the program in decision-tree.py and run it.
2. Change input values or sample data and observe the new output.
3. Create one example related to rule-based decisions.
4. Write 5 lines explaining the logic in your own words.

Summary

Decision Tree is not a theory-only topic. You should be able to explain the meaning, write the example, run it successfully, and use it in a small practical program.