When you build a decision model by hand, a single missing value can turn the whole prediction into a crash or a biased guess. This happens because naïve if‑else trees assume every feature is present and try to compare it directly with a threshold. What you'll learn: Why missing data breaks naïve if‑else trees How to add safe‑split handling without external libraries When pruning helps and when it hurts interpretability Understanding the Failure Mode A hand‑coded dec...