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python使用支持向量机算法分类

时间: 2023-10-05 09:14:56 浏览: 75

下面是使用支持向量机算法进行分类的Python代码示例：首先，我们需要导入必要的库： ```python from sklearn import svm from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score ``` 然后我们需要准备数据集，这里我们使用`make_classification`函数生成一个随机的二分类数据集： ```python X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=0, random_state=42) ``` 将数据集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) ``` 现在我们可以创建一个支持向量机分类器并使用训练集进行拟合： ```python clf = svm.SVC(kernel='linear') clf.fit(X_train, y_train) ``` 最后，我们可以使用测试集来评估分类器的性能并计算其准确度： ```python y_pred = clf.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print(f"Accuracy: {accuracy}") ``` 完整代码如下： ```python from sklearn import svm from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=0, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) clf = svm.SVC(kernel='linear') clf.fit(X_train, y_train) y_pred = clf.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print(f"Accuracy: {accuracy}") ``` 如果你想尝试不同的核函数，可以将`kernel`参数设置为`'rbf'`或`'poly'`。

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