AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
LLR2, a novel fully unsupervised concept drift detector based on Bayesian networks, is proposed, which computes a log-likelihood ratio that is evaluated against chi-squared quantiles for each variable.
A unified drift-aware framework for explainable diagnostics in deployed machine learning systems is proposed that can support proactive model monitoring and assist practitioners in deciding whether continued monitoring, investigation, recalibration, or model retraining is required.
Commonly, classifiers and monitoring procedures are trained from labeled data by optimizing an objective such as the misclassification rate. This may lead to unrepresentative conditional distributions of the outcome (the labels) given important external variables, different from the conditional laws in the population....
RiskBlend is proposed, a classifier-agnostic prioritization framework that combines four complementary risk signals: historical failure patterns, prediction shift, decision-boundary shift, and neighborhood change that achieves the highest average APFD in all 80 dataset-classifier-scenario combinations.
A structured multi-metric evaluation framework that integrates classification, ranking-based, calibration, and robustness-oriented metrics for comprehensive ML performance assessment is proposed capable of supporting more reliable real-world ML deployment.
These results support a scoped monitoring strategy for similar tabular settings: confidence-derived scores are effective for pointwise screening, whereas group-aware explanation audits provide complementary evidence about stable but incorrect feature reliance.
An uncertainty-aware machine learning framework that integrates probabilistic modeling techniques into conventional predictive architectures to jointly estimate epistemic and aleatoric uncertainty is proposed, indicating that incorporating uncertainty estimation enhances trustworthiness and robustness without sacrifici...
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