如何在python中实现多类SVM

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import classification_report

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

# 数据预处理
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42)

# 创建SVM分类器，这里使用默认的'RBF'核函数
svm_classifier = SVC(kernel='rbf', decision_function_shape='ovr')

# 训练模型
svm_classifier.fit(X_train, y_train)

# 预测测试集
y_pred = svm_classifier.predict(X_test)

# 输出分类报告
print(classification_report(y_test, y_pred))