用sklearn 实现svm
时间: 2023-05-24 09:02:35 浏览: 63
A:以下是使用sklearn实现SVM的代码示例:
首先,我们需要导入必要的库和数据集:
```Python
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
# 加载数据集
iris = datasets.load_iris()
X = iris.data
y = iris.target
# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
然后,我们可以实例化SVC并拟合数据:
```Python
# 实例化SVC类
svm_model = SVC(kernel='linear')
# 拟合训练数据
svm_model.fit(X_train, y_train)
```
接下来,我们可以使用测试集进行预测并计算准确率:
```Python
# 使用测试集进行预测并计算准确率
y_pred = svm_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
# 输出准确率
print('Accuracy:', accuracy)
```
完整代码如下:
```Python
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
# 加载数据集
iris = datasets.load_iris()
X = iris.data
y = iris.target
# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 实例化SVC类
svm_model = SVC(kernel='linear')
# 拟合训练数据
svm_model.fit(X_train, y_train)
# 使用测试集进行预测并计算准确率
y_pred = svm_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
# 输出准确率
print('Accuracy:', accuracy)
```