通过导入必要的scikit-learn导入必要的库，加载给定的数据，划分测试集和训练集之后训练预测和评估即可

具体代码如下：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

# 加载鸢尾花数据集
iris = load_iris()
X = iris.data
y = iris.target

# 划分数据集为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 标准化数据
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# 创建KNN分类器并训练模型
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)

# 使用测试集进行预测
y_pred = knn.predict(X_test)

# 输出结果
print("Accuracy:", accuracy_score(y_test, y_pred))
print("Classification Report:")
print(classification_report(y_test, y_pred, target_names=iris.target_names))
print("Confusion Matrix:")
print(confusion_matrix(y_test, y_pred))

运行结果：