Random Forests

Many trees = Better predictions.

What is Random Forest?

Collection of many decision trees working together!

Like asking 100 experts instead of 1.

How It Works

1. Create many decision trees
2. Each tree votes on answer
3. Majority vote wins

Example

Predicting if customer will buy product in Austin:

• Tree 1: Yes
• Tree 2: No
• Tree 3: Yes
• Tree 4: Yes
• Tree 5: Yes

Final answer: Yes (3/5 voted yes)

Python Code

from sklearn.ensemble import RandomForestClassifier

# Customer data: [age, income_k]
X = [[25, 40], [30, 60], [35, 80], [40, 100]]
y = [0, 0, 1, 1]  # 0=No buy, 1=Buy

# Train with 100 trees
model = RandomForestClassifier(n_estimators=100)
model.fit(X, y)

# Predict
customer = [[32, 70]]
prediction = model.predict(customer)
print("Will buy!" if prediction[0] == 1 else "Won't buy")

Why Better Than Single Tree?

• Less overfitting
• More stable
• More accurate
• Handles noise better

Disadvantages

• Slower to train
• Harder to interpret
• Uses more memory

Feature Importance

Random Forest shows which features matter most!

importances = model.feature_importances_
print(f"Age importance: {importances[0]}")
print(f"Income importance: {importances[1]}")

Remember

• Very powerful algorithm
• Great for most problems
• Trade-off: accuracy vs speed