Exploring the math of thresholds
summary
Claims that static thresholds converge with sampling while dynamic thresholds cut false positives using feedback and multivariate models. Demonstrations use IQR filtering, percentiles (P90, P99.999), standard deviation and the Law of Large Numbers to show cronjob experiments where more iterations reduce variance. Dynamic methods include curve fitting with A,B,C (70% fit reported, low σ), and ML options: logistic regression, kNN, SVM, isolation forest and PCA/DBSCAN. Practical: iterative numeric methods produce actionable thresholds for autoscaling and anomaly detection.
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chapters (9)
- 0:00Introduction to Thresholds
- 0:40Understanding Static Thresholds
- 2:28Process of Implementing Static Thresholds
- 4:27Law of Large Numbers in Thresholds
- 9:00Dynamic Thresholds and Anomaly Detection
- 9:59Curve Fitting for Dynamic Thresholds
- 12:25Machine Learning Methods for Dynamic Thresholds
- 13:10Summary and Practical Applications
- 14:23Conclusion and Final Thoughts
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