42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
# 导入必要的库
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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.preprocessing import StandardScaler
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.metrics import classification_report, accuracy_score
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import matplotlib.pyplot as plt
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# 1. 加载数据集
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iris = load_iris()
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X = iris.data # 特征数据
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y = iris.target # 目标标签
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# 2. 划分数据集（80%训练，20%测试）
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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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# 3. 特征标准化（很重要，KNN对特征尺度敏感）
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scaler = StandardScaler()
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X_train = scaler.fit_transform(X_train)
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X_test = scaler.transform(X_test)
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# 4. 构建KNN分类模型（这里K=3）
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knn = KNeighborsClassifier(n_neighbors=3)
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# 5. 训练模型
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knn.fit(X_train, y_train)
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# 6. 预测
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y_pred = knn.predict(X_test)
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# 7. 评估模型性能
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print("Accuracy:", accuracy_score(y_test, y_pred))
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print("Classification Report:")
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print(classification_report(y_test, y_pred))
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# 可视化（仅用于二维数据的情况）
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred, cmap='viridis', marker='o', label='Predictions')
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plt.xlabel("Feature 1")
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plt.ylabel("Feature 2")
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plt.title("KNN Classification (Predictions)")
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plt.savefig("./output/knn_c.png")
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