Hey Ahmet, appreciate you coming back with more.

on the sample size thing, you're completely right and I should've flagged it. did the math: at n=30, one sample is 3.33 points of accuracy, so standard error is around 3.3 points, meaning a rough 95% CI is something like ±6.4 points on any single accuracy number here. that's a genuinely useful thing to sit with, small differences between models on iris are basically noise, and it's another reason the cross-validation fix from your last comment matters even more than I framed it.

on the train/test gap, mostly agree but checked the actual numbers and there's a nuance worth mentioning. the tree in the post is capped at max_depth=3, and that one gets 98.33% train vs 96.67% test, a 1.67 point gap, not dramatic. the "100% on train" thing you're describing shows up if you let the tree grow unrestricted (max_depth=None), which in my case hits exactly 100% train vs 93.33% test, a 6.67 point gap. so the principle is dead on, just wanted to be precise about which version of the tree actually does that.

added a note on the test set size to the post, since that one directly changes how much trust to put in the numbers already there. leaving the train/test gap as something to carry forward rather than folding it into the post itself, it's a good diagnostic but this one's meant to stay intro-level and I don't want to stack on more than it needs. appreciate you pushing on both points though.