Decision Tree Explorer

A botanical research station has collected measurements from 150 iris flowers across three species. The field biologists can tell them apart by sight, but they need an automated system for their remote sensors. Your job: build a decision tree classifier that predicts species from petal and sepal measurements, then evaluate how well it performs using precision, recall, and accuracy. Understanding these metrics is critical — in the real world, not all mistakes cost the same.