Comparitive Analysis .pptx Footprinting, Enumeration, Scanning, Sniffing, Social Engineering
- 1. Bagging and Random Forest Theory and Applications in Machine Learning
- 2. Agenda • Introduction to Bias-Variance Tradeoff • Overfitting and Tree Pruning • Ensemble Learning Overview • Reduction in Variance • Bagging and Bootstrapping • Random Forest Algorithm • Sampling Features at Each Node • Extensions and Practical Applications
- 3. The Bias-Variance Tradeoff • Bias: Simplistic assumptions lead to under fitting. • Variance: Complex models over fit the training data. • Tradeoff: The goal is to minimize both for optimal performance.
- 4. Overfitting in Decision Trees • Deep decision trees capture noise, leading to overfitting. • Overfitting decreases test set accuracy despite high training accuracy.
- 5. Tree Pruning • Pre-pruning: Stops tree growth early. • Post-pruning: Removes non-essential branches.
- 6. Ensemble Learning Overview • Bagging: Reduces variance. • Boosting: Reduces bias.
- 7. Reduction in Variance • Variance is reduced by averaging predictions across models. • Bagging and Random Forest are designed to reduce variance.
- 8. Bagging and Bootstrapping • Bagging: Combines Bootstrap (sampling with replacement) and Aggregation (averaging predictions). Workflow • 1. Create multiple bootstrapped datasets. • 2. Train base models on each dataset. • 3. Aggregate results.
- 9. Random Forest Algorithm • An extension of Bagging using decision trees. • Randomly selects features for each split, decorrelating trees. • Aggregates predictions via voting or averaging.
- 10. Sampling Features at Each Node • Feature Selection: Random subset of features at each split. Benefits: • Reduces correlation among trees. • Increases diversity and accuracy.
- 11. Extensions to Random Forest • Extra Trees: Uses all data for splits and randomizes thresholds. • Gradient Boosted Trees: Sequentially builds trees to reduce errors.
- 12. Practical Applications of Random Forests • Classification: Fraud detection, medical diagnostics. • Regression: Sales forecasting, stock price prediction. • Time Series: Modeling temporal trends.
- 13. Performance Comparison • Decision Trees: High interpretability but prone to overfitting. • Random Forest: Robust and accurate, less interpretable.
- 14. Python Implementation Overview • Load data and preprocess. • Train RandomForestClassifier. • Evaluate feature importance.
- 15. Code Walkthrough • from sklearn.ensemble import RandomForestClassifier • model = RandomForestClassifier(n_estimators=100) • model.fit(X_train, y_train) • print(model.feature_importances_)
- 16. Tuning Random Forests Key Parameters: • n_estimators: Number of trees. • max_depth: Maximum depth of trees. • Tools: Grid search, cross-validation.
- 17. Limitations of Random Forest • Computationally intensive for large datasets. • Less interpretable than single decision trees.
- 18. Conclusion and Q&A • Summary of key points. • Thank the audience and invite questions.
- 19. Future Work Future work includes hyperparameter tuning for Bagging and Random Forest, testing on larger datasets, and exploring advanced ensemble methods like Gradient Boosting.
- 20. Thank You