34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
from sklearn import datasets
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.svm import SVC
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from sklearn.metrics import classification_report, confusion_matrix
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# 加载数据集
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iris = datasets.load_iris()
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X = iris.data
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y = iris.target
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# 将数据集拆分为训练集和测试集
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
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# 特征缩放（对于SVM来说很重要）
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scaler = StandardScaler()
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# fit_transform 计算数据集的参数,然后应用这是参数来转换数据
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X_train = scaler.fit_transform(X_train)
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# transform 使用前面fit学习到的参数来转换测试集参数.
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X_test = scaler.transform(X_test)
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# 创建SVM分类器
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svm_classifier = SVC(kernel='linear') # 你可以尝试其他内核，如'rbf', 'poly'等
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# 训练分类器
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svm_classifier.fit(X_train, y_train)
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# 对测试集进行预测
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y_pred = svm_classifier.predict(X_test)
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# 输出分类结果
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print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
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print("\nClassification Report:\n", classification_report(y_test, y_pred)) |