机器学习 实验三

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# 导入必要的库 import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split, cross_val_score, cross_validate from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix import seaborn as sns   # （1）加载iris数据集，并留出1/3的样本作为测试集 iris = load_iris() X = iris.data y = iris.target   # 使用留出法，留出1/3的样本作为测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3, random_state=42, stratify=y) # （2）使用训练集训练分类带有预剪枝和后剪枝的决策树模型 # 预剪枝可以通过设置max_depth参数来实现 # 后剪枝可以通过设置min_samples_split和min_samples_leaf参数来实现 clf_pre_pruning = DecisionTreeClassifier(max_depth=3, random_state=42) clf_pre_pruning.fit(X_train, y_train)   # （3）使用五折交叉验证对模型性能进行评估和选择 scores = cross_validate(clf_pre_pruning, X_train, y_train, cv=5,                         scoring=['accuracy', 'precision_macro', 'recall_macro', 'f1_macro'])   # 打印交叉验证结果 print("五折交叉验证结果：") print(f"准确度：{scores['test_accuracy'].mean():.4f} ± {scores['test_accuracy'].std():.4f}") print(f"精度：{scores['test_precision_macro'].mean():.4f} ± {scores['test_precision_macro'].std():.4f}") print(f"召回率：{scores['test_recall_macro'].mean():.4f} ± {scores['test_recall_macro'].std():.4f}") print(f"F1值：{scores['test_f1_macro'].mean():.4f} ± {scores['test_f1_macro'].std():.4f}")   # （4）使用测试集测试模型的性能 y_pred = clf_pre_pruning.predict(X_test)   # 计算测试集的性能指标 accuracy = accuracy_score(y_test, y_pred) precision = precision_score(y_test, y_pred, average='macro') recall = recall_score(y_test, y_pred, average='macro') f1 = f1_score(y_test, y_pred, average='macro')   # 打印测试集的性能指标 print("测试集性能指标：") print(f"准确度：{accuracy:.4f}") print(f"精度：{precision:.4f}") print(f"召回率：{recall:.4f}") print(f"F1值：{f1:.4f}")   # 绘制混淆矩阵 cm = confusion_matrix(y_test, y_pred) sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=iris.target_names, yticklabels=iris.target_names) plt.xlabel('Predicted') plt.ylabel('True') plt.show()