首页用python实现支持向量机对700个样本，每个样本五维特征的七分类任务

用python实现支持向量机对700个样本，每个样本五维特征的七分类任务

时间: 2024-04-30 08:22:37 浏览: 6

首先，需要导入所需的库和数据集： ```python from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.svm import SVC X, y = make_classification(n_samples=700, n_features=5, n_classes=7, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) ``` 接下来，定义一个支持向量机模型，并使用训练集进行拟合： ```python svm = SVC(kernel='linear', C=1) svm.fit(X_train, y_train) ``` 最后，使用测试集进行预测，并输出模型的准确率： ```python y_pred = svm.predict(X_test) accuracy = svm.score(X_test, y_test) print("Accuracy:", accuracy) ``` 完整代码如下： ```python from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.svm import SVC X, y = make_classification(n_samples=700, n_features=5, n_classes=7, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) svm = SVC(kernel='linear', C=1) svm.fit(X_train, y_train) y_pred = svm.predict(X_test) accuracy = svm.score(X_test, y_test) print("Accuracy:", accuracy) ```