线性核函数训练sklearn
时间: 2023-06-26 22:09:51 浏览: 49
使用线性核函数训练sklearn的支持向量机(SVM)模型非常简单。以下是一个例子:
首先,导入需要的库和数据集:
```python
from sklearn.datasets import load_iris
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
iris = load_iris()
X, y = iris.data, iris.target
```
接下来,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=42)
```
然后,创建SVM模型并使用线性核函数进行训练:
```python
clf = SVC(kernel='linear')
clf.fit(X_train, y_train)
```
最后,使用测试集进行预测并计算准确率:
```python
from sklearn.metrics import accuracy_score
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
```
完整代码如下:
```python
from sklearn.datasets import load_iris
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
clf = SVC(kernel='linear')
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
```