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