【机器学习sklearn实战】逻辑回归（Logistic regression）

官网教程：logistic-regression — scikit-learn 1.5.1 documentation

一 导入包

# 导入包
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report
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二 数据加载

# 加载鸢尾花数据集
iris = load_iris()
X = iris.data
y = iris.target
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三 数据划分

# 将数据划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
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四 模型创建

# 创建逻辑回归模型实例
logistic_regression = LogisticRegression(max_iter=10, random_state=42)
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五 模型训练

# 预测测试集上的标签
y_pred = logistic_regression.predict(X_test)
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六 模型评估

# 输出预测准确率
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.4f}")

# 输出详细的分类报告
report = classification_report(y_test, y_pred)
print("Classification Report:")
print(report)

# 查看模型系数
coefficients = logistic_regression.coef_
print("Coefficients:")
print(coefficients)

# 查看截距
intercept = logistic_regression.intercept_
print("Intercept:")
print(intercept)
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