机器学习 实验二

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from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split, cross_validate from sklearn.linear_model import LogisticRegression from sklearn.metrics import make_scorer, accuracy_score, precision_score, recall_score, f1_score, classification_report   # (1) 从 scikit-learn 库中加载 iris 数据集，使用留出法留出 1/3 的样本作为测试集（注意同分布取样） # 加载数据集 iris = load_iris() X, y = iris.data, iris.target   # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3, random_state=42, stratify=y)   # (2) 使用训练集训练对数几率回归（逻辑回归）分类算法 # 创建逻辑回归模型实例 model = LogisticRegression(solver='lbfgs', max_iter=200)   # 训练模型 model.fit(X_train, y_train)   # (3) 使用五折交叉验证对模型性能（准确度、精度、召回率和 F1 值）进行评估和选择 # 定义多个评分器 scoring = {     'accuracy': make_scorer(accuracy_score),     'precision': make_scorer(precision_score, average='weighted'),     'recall': make_scorer(recall_score, average='weighted'),     'f1': make_scorer(f1_score, average='weighted') }   # 使用交叉验证评估模型 scores = cross_validate(model, X_train, y_train, cv=5, scoring=scoring)   # 输出各项指标的平均值 print("五折交叉验证结果：") for key in scores:     if key.startswith('test_'):         print(f"{key[5:]}: {scores[key].mean():.4f}")  # 去掉前缀 'test_' 来显示指标名称   # (4) 使用测试集，测试模型的性能，对测试结果进行分析，完成实验报告中实验二的部分 # 预测测试集标签 y_pred = model.predict(X_test)   # 打印分类报告 report = classification_report(y_test, y_pred, target_names=iris.target_names, output_dict=True) print("\n测试集上的分类报告：") for label, metrics in report.items():     if isinstance(metrics, dict):         print(f"类别 {label}:")         for metric, value in metrics.items():             print(f"  {metric}: {value:.4f}")     else:         print(f"{label}: {value:.4f}")   # 打印混淆矩阵 from sklearn.metrics import confusion_matrix conf_matrix = confusion_matrix(y_test, y_pred) print("\n混淆矩阵：") print(conf_matrix)