Homin Lee discusses the importance of identifying unhealthy hosts in an infrastructure and introduces outlier detection, a technique that can help minimize service degradation and disruption. He explains that traditional threshold-based alerts can be difficult to define and may trigger false alarms, especially for metrics with spikes or fluctuating baselines. Instead, outlier detection uses algorithms such as DBSCAN and MAD to compare each host against others in the group, alerting when a host deviates from the pack while avoiding false positives. The two algorithms differ in their approach: DBSCAN uses clustering to identify outliers, while MAD is a robust measure of variability that focuses on deviations from the median. Homin Lee provides guidance on setting parameters and tuning tolerance for each algorithm, as well as considerations for choosing between DBSCAN and MAD depending on the specific use case. Ultimately, outlier detection can be used in conjunction with other monitoring features to provide dynamic alerts and minimize service disruption.