Python实现支持向量机（SVM）二分类算法可按如下步骤进行： ### 1. 环境准备 确保安装所需的Python库，如`numpy`、`scikit-learn`和`matplotlib`。以下是安装命令： ```bash pip install numpy scikit-learn matplotlib ``` ### 2. 导入必要的库 ```python import numpy as np from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.svm import SVC import matplotlib.pyplot as plt ``` ### 3. 加载数据集 以鸢尾花数据集为例，选取其中两类进行二分类： ```python # 加载鸢尾花数据集 iris = datasets.load_iris() # 选择前两类 X = iris.data[:100, :2] y = iris.target[:100] ``` ### 4. 划分训练集和测试集 ```python X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) ``` ### 5. 创建SVM分类器 ```python # 创建SVM分类器，使用线性核 clf = SVC(kernel='linear') ``` ### 6. 训练模型 ```python # 训练模型 clf.fit(X_train, y_train) ``` ### 7. 模型预测 ```python # 对测试集进行预测 y_pred = clf.predict(X_test) ``` ### 8. 评估模型 ```python from sklearn.metrics import accuracy_score # 计算准确率 accuracy = accuracy_score(y_test, y_pred) print(f"模型准确率: {accuracy}") ``` ### 9. 可视化决策边界（可选） ```python # 创建网格点 h = .02 x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) # 绘制决策边界和数据点 plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8) plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.coolwarm, edgecolors='k') plt.xlabel('Sepal length') plt.ylabel('Sepal width') plt.title('SVM Decision Boundary') plt.show() ```