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Cascading Confidence Ensemble (CCE*)

A multi-level classification system that cascades through increasingly complex models based on confidence thresholds. The system combines Logistic Regression, Random Forest, and XGBoost in a hierarchical ensemble that optimizes both accuracy and processing efficiency.

Challenge

Create a classification system that balances accuracy with computational efficiency by using simpler models for confident predictions while reserving complex models for uncertain cases.

Solution

Developed a three-level cascading system where predictions flow through models of increasing complexity based on confidence thresholds, with comprehensive metrics tracking and analysis at each level.

Implementation

  • Created three-tier model architecture (Logistic Regression, Random Forest, XGBoost)
  • Implemented confidence-based decision routing with customizable thresholds
  • Built SMOTE-based class imbalance handling
  • Developed comprehensive performance metrics for each level
  • Integrated processing time analysis and monitoring
  • Created extensive visualization system for model insights

Simulation Analysis

Multi-Level Classification

Visualization of the cascading confidence system showing decision flow through different model levels and performance metrics

Multi-Level Classification Simulation
Level 1 (Logistic)
Accuracy: 94.2%Latency: 12ms
Level 2 (Random Forest)
Accuracy: 96.8%Latency: 35ms
Level 3 (XGBoost)
Accuracy: 98.5%Latency: 65ms

Technical Architecture

CCE* Architecture

Core components of the Cascading Confidence Ensemble system

CCE* Architecture

Classification Process

Sequence diagram showing the cascading classification workflow

Classification Process

Key Metrics

3 tiers
Model Levels
0.8-0.9
Confidence Thresholds
<100ms
Processing Time
>0.95
AUC Score

Project Links

Technologies

PythonScikit-learnXGBoostSMOTEPandasSeaborn