Random Forest Algorithm

Random Forest क्या है?

Random Forest ka matlab hai: Random Forest combines many decision trees to produce a more stable and accurate prediction. Simple words me, ye topic practical Python programs likhne me direct use hota hai.

Is topic ko sirf definition ke liye nahi, balki stable predictions jaise real examples ke liye practice karein.

यह क्यों सीखना जरूरी है?

• Ye stable predictions me kaam aata hai.
• Ye feature importance analysis se bhi connected hai.
• Isse aap code ka output aur errors better samajh paate hain.

Important Terms

Syntax / Basic Pattern

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

from sklearn.ensemble import RandomForestClassifier
X = [[1, 80], [2, 82], [5, 90], [6, 92]]
y = [0, 0, 1, 1]
model = RandomForestClassifier(n_estimators=20, random_state=0)
model.fit(X, y)
print(model.predict([[5, 88]])[0])

Complete Example Program

from sklearn.ensemble import RandomForestClassifier

X = [[1, 80], [2, 82], [5, 90], [6, 92]]
y = [0, 0, 1, 1]

model = RandomForestClassifier(n_estimators=20, random_state=0)
model.fit(X, y)
print(model.predict([[5, 88]])[0])

Expected Output

Program Explanation

• from sklearn.ensemble import RandomForestClassifier imports ready-made features from a module/library.
• X = [[1, 80], [2, 82], [5, 90], [6, 92]] stores a value in X.
• y = [0, 0, 1, 1] stores a value in y.
• model = RandomForestClassifier(n_estimators=20, random_state=0) stores a value in model.
• model.fit(X, y) performs the next step of the program logic.
• print(model.predict([[5, 88]])[0]) displays information or calculated result on the screen.

Practical Uses

• Stable predictions.
• Feature importance analysis.
• Classification and regression.

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. Program ko random-forest.py file me type karke run karein.
2. Values change karke output compare karein.
3. stable predictions par ek छोटा example banayen.
4. Logic ko apne words me 5 lines me likhein.

सारांश

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