Tabular Classification - Random Forest

This case study demonstrates training a Random Forest classifier to predict passenger survival on the famous Titanic dataset. Random Forest is an ensemble learning method that combines multiple decision trees to create a robust, accurate classifier resistant to overfitting.

Dataset: Titanic Survival

• Source: Kaggle
• Type: Tabular classification
• Size: 891 passengers
• Features: Age, Sex, Passenger Class, Fare, Embarkation Port, etc.
• Target: Survived (0 = No, 1 = Yes)

Model Configuration

{
  "model": "random_forest",
  "category": "classification",
  "model_config": {
    "n_estimators": 100,
    "max_depth": 10,
    "min_samples_split": 5,
    "min_samples_leaf": 2,
    "criterion": "gini",
    "random_state": 42
  }
}

Training Results

Feature Importance

Understanding which features most influenced survival predictions:

Confusion Matrix

Model performance across predicted vs actual survival:

ROC Curve

Receiver Operating Characteristic showing model discrimination ability:

Common Use Cases

• Customer Churn Prediction: Identify customers likely to leave
• Loan Default Risk: Assess creditworthiness of applicants
• Medical Diagnosis: Classify disease presence from patient data
• Quality Control: Detect defective products in manufacturing
• Fraud Detection: Identify suspicious transactions

Key Settings

Essential Parameters

• n_estimators: Number of trees (100-500 typical)
• max_depth: Maximum tree depth (controls overfitting)
• min_samples_split: Minimum samples to split a node
• criterion: Split quality measure (gini or entropy)

Advanced Configuration

• class_weight: Handle imbalanced datasets ("balanced" or custom)
• max_features: Features to consider per split
• bootstrap: Whether to use bootstrap samples
• oob_score: Out-of-bag score estimation

Performance Metrics

• Accuracy: 85.3% - Overall correct predictions
• Precision: 88.1% - Of predicted survivors, 88.1% actually survived
• Recall: 77.4% - Of actual survivors, 77.4% were identified
• F1 Score: 82.4% - Harmonic mean of precision and recall
• AUC-ROC: 0.89 - Strong discrimination ability

Tips for Success

1. Feature Engineering: Create meaningful features (e.g., family size, title extraction)
2. Handle Missing Data: Impute or remove missing values strategically
3. Encoding: Convert categorical variables to numerical (one-hot, label encoding)
4. Hyperparameter Tuning: Use grid search or random search for optimization
5. Cross-Validation: Validate on multiple folds to ensure generalization

Example Scenarios

Scenario 1: High-Class Female Passenger

• Features: Female, 1st class, age 28, fare $75
• Prediction: Survived (probability: 0.92)
• Reasoning: Women and children first policy, higher class priority

Scenario 2: Third-Class Male Passenger

• Features: Male, 3rd class, age 35, fare $8
• Prediction: Did not survive (probability: 0.78)
• Reasoning: Lower priority in evacuation, limited lifeboat access

Troubleshooting

Problem: Model overfitting (high training accuracy, low test accuracy)

• Solution: Reduce max_depth, increase min_samples_split, use fewer features

Problem: Imbalanced classes affecting predictions

• Solution: Set class_weight='balanced' or use SMOTE for oversampling

Problem: Training too slow with large dataset

• Solution: Reduce n_estimators, use max_samples for subsample training

Next Steps

After training your Random Forest model, you can:

• Deploy for real-time predictions
• Compare with other algorithms (XGBoost, SVM)
• Create ensemble models combining multiple classifiers
• Export feature importance for interpretability