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from sklearn.datasets import load_iris
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from sklearn.model_selection import train_test_split
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.metrics import accuracy_score
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# 加载Iris数据集
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iris = load_iris()
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X = iris.data
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y = iris.target
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# 将数据集拆分为训练集和测试集
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# 分类器
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knn = KNeighborsClassifier(n_neighbors=3)
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# 在训练集上训练模型
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knn.fit(X_train, y_train)
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# 在测试集上进行预测
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y_pred = knn.predict(X_test)
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# 计算准确率
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accuracy = accuracy_score(y_test, y_pred)
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print('准确率:', accuracy)
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