sklearnknn分类模型代码

可以使用下面的代码来实现使用sklearn库中的KNN分类模型:

# 导入所需依赖库
from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target

# 切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=0.2, random_state=42)

# 创建KNN分类模型
knn = KNeighborsClassifier(n_neighbors=5)

# 在训练集上训练模型
knn.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = knn.predict(X_test)

# 计算预测准确率
accuracy = accuracy_score(y_test, y_pred)

# 打印预测准确率
print("KNN分类模型的预测准确率为: ", accuracy)

在上述代码中，首先导入了所需的依赖库，然后加载了鸢尾花数据集。接下来，将数据集划分为训练集和测试集，然后创建了一个KNN分类模型并在训练集上进行训练。最后，使用训练好的模型对测试集进行预测，计算预测准确率并输出结果。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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