32 lines
994 B (Stored with Git LFS)
Python
32 lines
994 B (Stored with Git LFS)
Python
from sklearn.datasets import load_iris
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from sklearn.model_selection import train_test_split
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from sklearn.svm import SVC
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import argparse
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# 加载数据集
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iris = load_iris()
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X = iris.data
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y = iris.target
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# 划分数据集
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# 参数解析
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parser = argparse.ArgumentParser(description='SVM Model for Iris dataset')
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parser.add_argument('--C', type=float, default=1.0, help='Regularization parameter')
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parser.add_argument('--kernel', type=str, default='rbf', help='Kernel type')
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parser.add_argument('--gamma', type=float, default='scale', help='Kernel coefficient')
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args = parser.parse_args()
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# 定义支持向量机模型并设置参数
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svm = SVC(C=args.C, kernel=args.kernel, gamma=args.gamma)
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# 在训练集上拟合模型
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svm.fit(X_train, y_train)
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# 输出模型在测试集上的准确率
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test_accuracy = svm.score(X_test, y_test)
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print(f"Accuracy={test_accuracy}")
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