Why Spatial Leakage Makes ML Look Better Than It Is
Spatial data can make machine‑learning models look surprisingly accurate when evaluation ignores geography. The article shows how proximity, repeated‑asset structures, and uneven regional coverage create leakage traps that overstate performance, and offers concrete validation tricks to ensure models truly generalize across neighborhoods and market segments.