机器学习 实验一

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# 导入必要的库 import pandas as pd from sklearn.datasets import load_iris from sklearn.model_selection import cross_val_score, KFold, cross_val_predict from sklearn.linear_model import LogisticRegression from sklearn.metrics import classification_report   # (1) 使用 pandas 从本地读取 Iris 数据集 # data = pd.read_csv('iris.csv') # print(data)  # 打印数据集   # (2) 从 scikit-learn 直接加载 Iris 数据集 iris = load_iris() print("Iris 数据集：") print(iris.data)  # 打印数据集   # (3) 实现五折交叉验证进行模型训练 # 定义模型 model = LogisticRegression(max_iter=200)   # 定义交叉验证方法 kf = KFold(n_splits=5, shuffle=True, random_state=42)   # 进行五折交叉验证 scores = cross_val_score(model, iris.data, iris.target, cv=kf, scoring='accuracy')   # 计算预测值 y_pred = cross_val_predict(model, iris.data, iris.target, cv=kf)   # (4) 计算并输出模型的准确度、精度、召回率和 F1 值 # 输出交叉验证的准确度 print("交叉验证准确度分数：", scores) print("平均准确度：", scores.mean())   # 输出分类报告 target_names_chinese = ['山鸢尾', '变色鸢尾', '维吉尼亚鸢尾'] report_chinese = classification_report(iris.target, y_pred, target_names=target_names_chinese)   # 解析分类报告，提取精度、召回率和 F1 值 report_lines = report_chinese.split('\n') for line in report_lines:     if line.strip() and not line.startswith('avg') and not line.startswith('micro') and not line.startswith('macro') and not line.startswith('weighted'):         parts = line.split()         if len(parts) == 5:             class_name = parts[0]             precision = float(parts[1])             recall = float(parts[2])             f1_score = float(parts[3])             support = int(parts[4])             print(f"类别: {class_name}, 精度: {precision:.2f}, 召回率: {recall:.2f}, F1 值: {f1_score:.2f}, 支持数: {support}")   print("中文分类报告：\n", report_chinese)