🟣 ML + AI · Lesson 52

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.

💡 At a Glance

Point	Details
Course Area	Machine Learning + AI Concepts used for prediction, classification, clustering and AI-based projects.
Main Use	rule-based decisions
Example File	`decision-tree.py`
Practice Focus	Run, change values, and explain the output line by line.

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.

Term	Meaning
tree	tree is an important term in this topic.
root node	root node is an important term in this topic.
leaf node	leaf node is an important term in this topic.
classification	Predicting a category or class.
rules	rules is an important term in this topic.

Syntax / Basic Pattern

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

Basic Pattern

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

Python – decision-tree.py

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

Pass

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

Type the program in decision-tree.py and run it.
Change input values or sample data and observe the new output.
Create one example related to rule-based decisions.
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.

Decision Tree क्या है?

Decision Tree ka matlab hai: A Decision Tree makes predictions using a tree-like structure of questions and decisions. Simple words me, ye topic practical Python programs likhne me direct use hota hai.

Is topic ko sirf definition ke liye nahi, balki rule-based decisions jaise real examples ke liye practice karein.

यह क्यों सीखना जरूरी है?

Ye rule-based decisions me kaam aata hai.
Ye explainable classification se bhi connected hai.
Isse aap code ka output aur errors better samajh paate hain.

Important Terms

Term	Meaning
tree	tree is an important term in this topic.
root node	root node is an important term in this topic.
leaf node	leaf node is an important term in this topic.
classification	Predicting a category or class.
rules	rules is an important term in this topic.

Syntax / Basic Pattern

Basic idea: pehle data तैयार करें, phir Python logic apply करें, aur finally result display करें.

Basic Pattern

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

Python – decision-tree.py

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

Pass

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.

Practical Uses

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

Program ko decision-tree.py file me type karke run karein.
Values change karke output compare karein.
rule-based decisions par ek छोटा example banayen.
Logic ko apne words me 5 lines me likhein.

सारांश

Decision Tree ko tab complete maanenge jab aap iska meaning, example, output aur practical use clearly explain kar saken.

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