simple_knn_classification_report.py

# bibliotecas
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report

# remove warnings
import warnings
warnings.filterwarnings("ignore")

# dataset
iris = load_iris()

# features e target
X = iris.data
y = iris.target

# treino e teste
X_train, X_test, y_train, y_test = train_test_split(X,y)

# treinando o modelo
model = KNeighborsClassifier()
model.fit(X_train,y_train)

# predizendo o teste
y_pred = model.predict(X_test)

# comparando predição com o real
print(classification_report(y_test, y_pred))