🟪 1-Minute Summary
Random Forest builds multiple decision trees on random subsets of data and features, then averages predictions (regression) or votes (classification). Bagging reduces variance and overfitting. Pros: high accuracy, handles non-linearity, robust to outliers, feature importance. Cons: less interpretable, slower, memory intensive. Often a go-to algorithm for tabular data.
🟦 Core Notes (Must-Know)
How Random Forest Works
[Content to be filled in]
Key Hyperparameters
[Content to be filled in]
- n_estimators (number of trees)
- max_depth
- max_features
- min_samples_split
Bagging Explained
[Content to be filled in]
Feature Importance
[Content to be filled in]
When to Use Random Forest
[Content to be filled in]
🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
-
“How does Random Forest work?”
- [Answer: Build many trees on random samples, aggregate predictions]
-
“What makes it ‘random’?”
- [Answer: Random sample of data AND random subset of features]
-
“Random Forest vs single Decision Tree?”
- [Answer: RF reduces overfitting, more stable, but less interpretable]
-
“How do you interpret feature importance?”
- [Answer framework to be filled in]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Using default n_estimators (too few)
[Content to be filled in]
Mistake 2: Not scaling features
[Content to be filled in - not required but can help]
🟩 Mini Example (Quick Application)
Scenario
[Classification with feature importance]
Solution
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
# Example to be filled in
🔗 Related Topics
Navigation: