用支持向量机导入数据进行交叉验证的代码
时间: 2023-12-11 21:51:34 浏览: 30
以下是使用支持向量机进行数据导入和交叉验证的Python代码示例:
首先,导入必要的库和数据集:
```python
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
iris = datasets.load_iris() # 加载鸢尾花数据集
X = iris.data # 特征
y = iris.target # 目标变量
```
然后,将数据集拆分为训练集和测试集:
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
```
接下来,进行特征缩放:
```python
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
```
最后,使用SVM进行分类,同时进行交叉验证:
```python
from sklearn.model_selection import cross_val_score
svm = SVC(kernel='linear', C=1, random_state=0) # 创建SVM分类器
scores = cross_val_score(svm, X_train, y_train, cv=10) # 进行10折交叉验证
print('Cross-validation scores: {}'.format(scores))
print('Average score: {:.2f}'.format(np.mean(scores)))
```
这样就可以使用支持向量机对数据进行分类和交叉验证了。