Supervised vs Unsupervised

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.tree import DecisionTreeClassifier
X,y=load_iris(return_X_y=True)
Xtr,Xte,ytr,yte=train_test_split(X,y,test_size=.2,stratify=y,random_state=0)
for d in [1,3,None]:
    m=DecisionTreeClassifier(max_depth=d,random_state=0).fit(Xtr,ytr)
    print(f"depth={d}: train={m.score(Xtr,ytr):.2f} test={m.score(Xte,yte):.2f} cv={cross_val_score(m,X,y,cv=5).mean():.2f}")