Naive Bayes Spam Detection
MATLAB-based email classifier achieving 91.6% ROC-AUC with hyperparameter optimization and bootstrap validation
Technology Stack
MATLABNaive BayesLogistic RegressionUCI SpambaseBootstrap CICross-Validation
ML Frameworks
Deep Learning
NLP
Deployment
ROI
Email security automation
Accuracy
85.94% test accuracy
Key Performance Indicators
Precision
0.838Recall
0.797F1-Score
0.817Business Impact Summary
| Return on Investment | Email security automation |
| Model Accuracy | 85.94% test accuracy |
| Precision | 0.838 |
| Recall | 0.797 |
| F1-Score | 0.817 |
Overview
Production-ready email spam classifier using Naive Bayes on UCI Spambase dataset (4,601 emails, 57 features). Features hyperparameter optimization, 5-fold cross-validation, and bootstrap confidence intervals.
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