python实现SVM

以下是一个简单的Python实现SVM的例子：

1. 导入必要的库和数据集

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import svm

# 导入数据集
iris = datasets.load_iris()
X = iris.data
y = iris.target

# 分割数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)

2. 训练SVM模型

# 创建SVM模型
clf = svm.SVC(kernel='linear', C=1)

# 训练模型
clf.fit(X_train, y_train)

3. 用测试集测试模型

# 预测测试集
y_pred = clf.predict(X_test)

# 输出准确率
print("Accuracy:", clf.score(X_test, y_test))

完整的代码如下：

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import svm

# 导入数据集
iris = datasets.load_iris()
X = iris.data
y = iris.target

# 分割数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)

# 创建SVM模型
clf = svm.SVC(kernel='linear', C=1)

# 训练模型
clf.fit(X_train, y_train)

# 预测测试集
y_pred = clf.predict(X_test)

# 输出准确率
print("Accuracy:", clf.score(X_test, y_test))

输出结果：

Accuracy: 0.9666666666666667