完成--逻辑回归示例

docs/detail.md

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+1.项目采用 python sklearn库开发.

logistic_regression.py

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+# 导入必要的库
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
+
+# 加载数据集
+iris = load_iris()
+X = iris.data[:, :2]  # 只使用前两个特征以便可视化
+y = (iris.target != 0) * 1  # 将目标变量转换为二分类问题（0和1）
+
+# 将数据集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)
+
+# 特征标准化
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+
+# 创建逻辑回归模型
+model = LogisticRegression()
+
+# 训练模型
+model.fit(X_train, y_train)
+
+# 在测试集上进行预测
+y_pred = model.predict(X_test)
+
+# 评估模型性能
+accuracy = accuracy_score(y_test, y_pred)
+conf_matrix = confusion_matrix(y_test, y_pred)
+class_report = classification_report(y_test, y_pred)
+
+print(f"Accuracy: {accuracy:.2f}")
+print("Confusion Matrix:")
+print(conf_matrix)
+print("Classification Report:")
+print(class_report)
+
+# 可视化决策边界
+def plot_decision_boundary(X, y, model):
+    h = .02  # 网格步长
+    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
+    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
+    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
+    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+    plt.contourf(xx, yy, Z, alpha=0.8)
+    plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', marker='o')
+    plt.xlabel('Feature 1')
+    plt.ylabel('Feature 2')
+    plt.title('Decision Boundary')
+    plt.savefig('./output/logistic.png')
+
+plot_decision_boundary(X_train, y_train, model)