svm多分类预测准确率python
时间: 2023-07-26 10:21:12 浏览: 52
你可以使用scikit-learn库中的SVM模型来进行多分类预测,并使用交叉验证计算准确率。下面是一个简单的示例代码:
```python
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
# 加载数据集
iris = datasets.load_iris()
X = iris.data
y = iris.target
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
# 创建SVM分类器
clf = SVC(kernel='linear', C=1, decision_function_shape='ovr')
# 交叉验证计算准确率
scores = cross_val_score(clf, X_train, y_train, cv=5)
print("交叉验证准确率: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
# 训练模型并预测
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print("测试集准确率:", accuracy)
```
其中,`cross_val_score`函数用于进行交叉验证计算准确率,`SVC`类用于创建SVM分类器,`accuracy_score`函数用于计算测试集准确率。你可以根据自己的数据集和需求调整代码。