使用sklearn的iris数据集选取data的前两列为特征target为类别标签。train_test_split用来分隔训练集和测试集train_size = c random_state = 1。使用sklearn的支持向量机核函数 rbf gamma系数 20 decision_function_shape 策略 ovo对训练集拟合而后测试训练集和测试集的精度。精度使用score显示2位小

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

# 加载数据集
iris = load_iris()
X = iris.data[:, :2]
y = iris.target

# 分割数据集
c = 0.6
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=c, random_state=1)

# 训练模型
model = SVC(kernel='rbf', gamma=20, decision_function_shape='ovo')
model.fit(X_train, y_train)

# 计算训练集和测试集的精度
train_accu = model.score(X_train, y_train)
test_accu = model.score(X_test, y_test)

# 输出精度结果
print("accu of model for train set is {:.2f}".format(train_accu))
print("accu of model for test set is {:.2f}".format(test_accu))