🎯 Goal
Learn how Random Forests combine many decision trees to make better, more stable predictions — reducing overfitting and increasing accuracy.
🧩 1. What Is a Random Forest?
A Random Forest is an ensemble of many decision trees, each trained on different subsets of the data and features.
Each tree gives its vote, and the forest decides by majority rule.
💡 Key idea:
“Several mediocre trees, when voting together, can become a brilliant model.”
🧠 2. Why Is It So Effective?
Advantage Explanation Less overfitting The errors of each tree cancel out across the ensemble. Higher accuracy Uses majority voting or average of predictions. More stability Random sampling makes results less sensitive to noise. Versatility Works for both classification and regression.
⚙️ 3. Practical Example in Python
from sklearn.ensemble import RandomForestClassifier
import numpy as np# Example data: [age, income, purchase_frequency]
X = np.array([
[22, 25, 2],
[25, 30, 3],
[47, 90, 8],
[52, 110, 9],
[46, 95, 7],
[56, 80, 5]
])
y = np.array([0, 0, 1, 1, 1, 1]) # 0 = No…
