metrics comparison of KNeighborClassifier and LinearRegression

ML - Metrics

from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris

iris = load_iris()
X = iris.data
y = iris.target

logreg = LogisticRegression()
logreg.fit(X,y)
ypred = logreg.predict(X)
print('logistic regression: ', metrics.accuracy_score(ypred,y))

knn5 = KNeighborsClassifier(n_neighbors=5)
knn5.fit(X,y)
kpred5 = knn5.predict(X)
print('KNeighbors 5 Classification: ', metrics.accuracy_score(kpred5,y))


knn1 = KNeighborsClassifier(n_neighbors=1)
knn1.fit(X,y)
kpred1 = knn.predict(X)
print('KNeighbors 1 Classification: ', metrics.accuracy_score(kpred1,y))