sklearn svm实现

要使用Scikit-learn库实现SVM，需要进行以下步骤：

1.导入所需的库：

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

2.加载数据集：

iris = datasets.load_iris()
X = iris.data
y = iris.target

3.将数据集分成训练集和测试集：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

4.创建SVM分类器对象：

svm = SVC(kernel='linear')

5.训练模型：

svm.fit(X_train, y_train)

6.使用训练好的模型进行预测：

y_pred = svm.predict(X_test)

7.计算模型的准确率：

acc = accuracy_score(y_test, y_pred)
print('Accuracy:', acc)

完整的代码如下所示：

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

iris = datasets.load_iris()
X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

svm = SVC(kernel='linear')
svm.fit(X_train, y_train)

y_pred = svm.predict(X_test)

acc = accuracy_score(y_test, y_pred)
print('Accuracy:', acc)