完成--svm分类示例

svm_classification.py

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