修改和补充下列代码得到十折交叉验证的平均auc值和平均aoc曲线，平均分类报告以及平均混淆矩阵 min_max_scaler = MinMaxScaler() X_train1, X_test1 = x[train_id], x[test_id] y_train1, y_test1 = y[train_id], y[test_id] # apply the same scaler to both sets of data X_train1 = min_max_scaler.fit_transform(X_train1) X_test1 = min_max_scaler.transform(X_test1) X_train1 = np.array(X_train1) X_test1 = np.array(X_test1) config = get_config() tree = gcForest(config) tree.fit(X_train1, y_train1) y_pred11 = tree.predict(X_test1) y_pred1.append(y_pred11 X_train.append(X_train1) X_test.append(X_test1) y_test.append(y_test1) y_train.append(y_train1) X_train_fuzzy1, X_test_fuzzy1 = X_fuzzy[train_id], X_fuzzy[test_id] y_train_fuzzy1, y_test_fuzzy1 = y_sampled[train_id], y_sampled[test_id] X_train_fuzzy1 = min_max_scaler.fit_transform(X_train_fuzzy1) X_test_fuzzy1 = min_max_scaler.transform(X_test_fuzzy1) X_train_fuzzy1 = np.array(X_train_fuzzy1) X_test_fuzzy1 = np.array(X_test_fuzzy1) config = get_config() tree = gcForest(config) tree.fit(X_train_fuzzy1, y_train_fuzzy1) y_predd = tree.predict(X_test_fuzzy1) y_pred.append(y_predd) X_test_fuzzy.append(X_test_fuzzy1) y_test_fuzzy.append(y_test_fuzzy1)y_pred = to_categorical(np.concatenate(y_pred), num_classes=3) y_pred1 = to_categorical(np.concatenate(y_pred1), num_classes=3) y_test = to_categorical(np.concatenate(y_test), num_classes=3) y_test_fuzzy = to_categorical(np.concatenate(y_test_fuzzy), num_classes=3) print(y_pred.shape) print(y_pred1.shape) print(y_test.shape) print(y_test_fuzzy.shape) # 深度森林 report1 = classification_report(y_test, y_prprint("DF",report1) report = classification_report(y_test_fuzzy, y_pred) print("DF-F",report) mse = mean_squared_error(y_test, y_pred1) rmse = math.sqrt(mse) print('深度森林RMSE:', rmse) print('深度森林Accuracy:', accuracy_score(y_test, y_pred1)) mse = mean_squared_error(y_test_fuzzy, y_pred) rmse = math.sqrt(mse) print('F深度森林RMSE:', rmse) print('F深度森林Accuracy:', accuracy_score(y_test_fuzzy, y_pred)) mse = mean_squared_error(y_test, y_pred) rmse = math.sqrt(mse) print('F?深度森林RMSE:', rmse) print('F?深度森林Accuracy:', accuracy_score(y_test, y_pred))

修改完善下列代码，得到十折交叉验证三分类的平均每一折的分类报告，平均每一折的混淆矩阵，平均每一折的auc值和roc曲线。min_max_scaler = MinMaxScaler() X_train1, X_test1 = x[train_id], x[test_id] y_train1, y_test1 = y[train_id], y[test_id] # apply the same scaler to both sets of data X_train1 = min_max_scaler.fit_transform(X_train1) X_test1 = min_max_scaler.transform(X_test1) # convert to numpy arrays X_train1 = np.array(X_train1) X_test1 = np.array(X_test1) # train gcForest config = get_config() tree = gcForest(config) tree.fit(X_train1, y_train1)

以下是修改后的代码： from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, roc_curve from sklearn.model_selection ...

修改完善下列代码，得到十折交叉验证三分类的平均每一折的分类报告，三分类的每一类的平均每一折的混淆矩阵，平均每一折的auc值和roc曲线。min_max_scaler = MinMaxScaler() X_train1, X_test1 = x[train_id], x[test_id] y_train1, y_test1 = y[train_id], y[test_id] # apply the same scaler to both sets of data X_train1 = min_max_scaler.fit_transform(X_train1) X_test1 = min_max_scaler.transform(X_test1) # convert to numpy arrays X_train1 = np.array(X_train1) X_test1 = np.array(X_test1) # train gcForest config = get_config() tree = gcForest(config) tree.fit(X_train1, y_train1)

以下是修改完善后的代码，其中包括了计算每一折的分类报告和混淆矩阵，以及计算平均每一折的AUC值和ROC曲线。 from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import confusion_matrix,...

修改和补充下列代码得到十折交叉验证的平均每一折auc值和平均每一折aoc曲线，平均每一折分类报告以及平均每一折混淆矩阵 min_max_scaler = MinMaxScaler() X_train1, X_test1 = x[train_id], x[test_id] y_train1, y_test1 = y[train_id], y[test_id] # apply the same scaler to both sets of data X_train1 = min_max_scaler.fit_transform(X_train1) X_test1 = min_max_scaler.transform(X_test1) X_train1 = np.array(X_train1) X_test1 = np.array(X_test1) config = get_config() tree = gcForest(config) tree.fit(X_train1, y_train1) y_pred11 = tree.predict(X_test1) y_pred1.append(y_pred11 X_train.append(X_train1) X_test.append(X_test1) y_test.append(y_test1) y_train.append(y_train1) X_train_fuzzy1, X_test_fuzzy1 = X_fuzzy[train_id], X_fuzzy[test_id] y_train_fuzzy1, y_test_fuzzy1 = y_sampled[train_id], y_sampled[test_id] X_train_fuzzy1 = min_max_scaler.fit_transform(X_train_fuzzy1) X_test_fuzzy1 = min_max_scaler.transform(X_test_fuzzy1) X_train_fuzzy1 = np.array(X_train_fuzzy1) X_test_fuzzy1 = np.array(X_test_fuzzy1) config = get_config() tree = gcForest(config) tree.fit(X_train_fuzzy1, y_train_fuzzy1) y_predd = tree.predict(X_test_fuzzy1) y_pred.append(y_predd) X_test_fuzzy.append(X_test_fuzzy1) y_test_fuzzy.append(y_test_fuzzy1)y_pred = to_categorical(np.concatenate(y_pred), num_classes=3) y_pred1 = to_categorical(np.concatenate(y_pred1), num_classes=3) y_test = to_categorical(np.concatenate(y_test), num_classes=3) y_test_fuzzy = to_categorical(np.concatenate(y_test_fuzzy), num_classes=3) print(y_pred.shape) print(y_pred1.shape) print(y_test.shape) print(y_test_fuzzy.shape) # 深度森林 report1 = classification_report(y_test, y_prprint("DF",report1) report = classification_report(y_test_fuzzy, y_pred) print("DF-F",report) mse = mean_squared_error(y_test, y_pred1) rmse = math.sqrt(mse) print('深度森林RMSE:', rmse) print('深度森林Accuracy:', accuracy_score(y_test, y_pred1)) mse = mean_squared_error(y_test_fuzzy, y_pred) rmse = math.sqrt(mse) print('F深度森林RMSE:', rmse) print('F深度森林Accuracy:', accuracy_score(y_test_fuzzy, y_pred)) mse = mean_squared_error(y_test, y_pred) rmse = math.sqrt(mse)

每一折都需要记录相应的分类报告、混淆矩阵、auc值和aoc曲线。以下是修改后的代码： from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import classification_report, confusion_matrix, ...

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data = pd.read_excel('C:/lydata/test4.xlsx') X = data.drop('HER2_G', axis=1) y = data['HER2_G'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=42) kf = KFold(n_splits=5, shuffle=True, random_state=42) accuracy_scores = [] precision_scores = [] recall_scores = [] f1_scores = [] auc_scores = [] total_confusion_matrix = np.zeros((len(np.unique(y_train)), len(np.unique(y_train))), dtype=int) pipeline = Pipeline([ ('smote', SMOTE(k_neighbors=3, sampling_strategy=0.8, random_state=42)), ('scaler', RobustScaler()), ('gb', GradientBoostingClassifier( learning_rate=0.04, n_estimators=829, subsample=0.79, min_samples_split=2, min_samples_leaf=1, max_depth=4, random_state=42, warm_start=True, tol=0.0001, ccp_alpha=0, max_features=10, )) ]) # 存储每个折的 FPR、TPR 和 AUC fprs = [] tprs = [] aucs = [] for train_index, val_index in kf.split(X_train): X_train_fold, X_val = X.iloc[train_index], X.iloc[val_index] y_train_fold, y_val = y.iloc[train_index], y.iloc[val_index] pipeline.fit(X_train_fold, y_train_fold) y_pred = pipeline.predict(X_val) y_proba = pipeline.predict_proba(X_val)[:, 1] accuracy_scores.append(accuracy_score(y_val, y_pred)) precision_scores.append(precision_score(y_val, y_pred)) recall_scores.append(recall_score(y_val, y_pred)) f1_scores.append(f1_score(y_val, y_pred)) auc_scores.append(roc_auc_score(y_val, y_proba)) cm = confusion_matrix(y_val, y_pred) total_confusion_matrix += cm # 计算 ROC 曲线所需指标 fpr, tpr, _ = roc_curve(y_val, y_proba) fprs.append(fpr) tprs.append(tpr) aucs.append(roc_auc_score(y_val, y_proba)) accuracy = np.mean(accuracy_scores) precision = np.mean(precision_scores) recall = np.mean(recall_scores) f1 = np.mean(f1_scores) auc = np.mean(auc_scores) print("Gradient Boosting 参数：") print(pipeline.named_steps['gb'].get_params()) print(f"Gradient Boosting 平均 accuracy: {accuracy:.2f}") print(f"Gradient Boosting 平均 precision: {precision:.2f}") print(f"Gradient Boosting 平均 recall: {recall:.2f}") print(f"Gradient Boosting 平均 F1 score: {f1:.2f}") print(f"Gradient Boosting 平均 AUC score: {auc:.2f}") print("综合混淆矩阵：") print(total_confusion_matrix) pipeline.fit(X_train, y_train) y_test_pred = pipeline.predict(X_test) y_test_proba = pipeline.predict_proba(X_test)[:, 1] accuracy_test = accuracy_score(y_test, y_test_pred) precision_test = precision_score(y_test, y_test_pred) recall_test = recall_score(y_test, y_test_pred) f1_test = f1_score(y_test, y_test_pred) auc_test = roc_auc_score(y_test, y_test_proba) print(f"测试集 accuracy: {accuracy_test:.2f}") print(f"测试集 precision: {precision_test:.2f}") print(f"测试集 recall: {recall_test:.2f}") print(f"测试集 F1 score: {f1_test:.2f}") print(f"测试集 AUC score: {auc_test:.2f}") cm_test = confusion_matrix(y_test, y_test_pred) print("测试集混淆矩阵：") print(cm_test) # 绘制各个折的 ROC 曲线 plt.figure(figsize=(10, 8)) for i in range(len(fprs)): plt.plot(fprs[i], tprs[i], label=f'Fold {i + 1} (AUC = {aucs[i]:.2f})') plt.plot([0, 1], [0, 1], color='navy', linestyle='--') plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('ROC Curve for Each Fold in 5-Fold Cross-validation') plt.legend(loc="lower right") plt.show() 这个代码我要画出五折交叉验证循环中的各个roc曲线，为什么有四折都是一

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193样本正负比2比三特征有53个，标签是乳腺癌患者her2是否表达大部分特征是心率变异的值，比如S1_Mean RR (ms) S1_SDNN (ms) S1_Mean HR (bpm) S1_SD HR (bpm) S1_Min HR (bpm) S1_Max HR (bpm)S1_RP_Lmean (beats) S1_RP_Lmax (beats) S1_RP_REC (%) S1_RP_DET (%) S1_RP_ShanEn S1_MSE_1 S1_MSE_2 S1_MSE_3 S1_MSE_4 S1_MSE_5 等,还有一些生理指标如年龄和bmi下面是我的数据操作和模型代码。写一个论文形式的模型搭建内容（根据我的数据好好仔细回答为什么使用gb模型，而不是其他如逻辑回归支持向量机和随机森林等，还有为什么使用管道和五折交叉验证）data = pd.read_excel(‘C:/lydata/test4.xlsx’) X = data.drop(‘HER2_G’, axis=1) y = data[‘HER2_G’] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=60) kf = KFold(n_splits=5, shuffle=True, random_state=91) accuracy_scores = [] precision_scores = [] recall_scores = [] f1_scores = [] auc_scores = [] total_confusion_matrix = np.zeros((len(np.unique(y_train)), len(np.unique(y_train))), dtype=int) pca = PCA(n_components=14) pipeline = Pipeline([ (‘scaler’, StandardScaler()), (‘smote’, SMOTE(k_neighbors=4, sampling_strategy=0.94, random_state=42)), (‘pca’, pca), (‘gb’, GradientBoostingClassifier( learning_rate=0.02, n_estimators=90, subsample=0.75, min_samples_split=5, min_samples_leaf=1, max_depth=6, random_state=42, warm_start=True, tol=0.0001, ccp_alpha=0, max_features=12, )) ]) for train_index, val_index in kf.split(X_train): X_train_fold, X_val = X_train.iloc[train_index], X_train.iloc[val_index] y_train_fold, y_val = y_train.iloc[train_index], y_train.iloc[val_index] pipeline.fit(X_train_fold, y_train_fold) y_pred = pipeline.predict(X_val) y_proba = pipeline.predict_proba(X_val)[:, 1] accuracy_scores.append(accuracy_score(y_val, y_pred)) precision_scores.append(precision_score(y_val, y_pred)) recall_scores.append(recall_score(y_val, y_pred)) f1_scores.append(f1_score(y_val, y_pred)) auc_scores.append(roc_auc_score(y_val, y_proba)) cm = confusion_matrix(y_val, y_pred) total_confusion_matrix += cm accuracy = np.mean(accuracy_scores) precision = np.mean(precision_scores) recall = np.mean(recall_scores) f1 = np.mean(f1_scores) auc = np.mean(auc_scores) print(“Gradient Boosting 参数：”) print(pipeline.named_steps[‘gb’].get_params()) print(f"Gradient Boosting 平均 accuracy: {accuracy:.2f}“) print(f"Gradient Boosting 平均 precision: {precision:.2f}”) print(f"Gradient Boosting 平均 recall: {recall:.2f}“) print(f"Gradient Boosting 平均 F1 score: {f1:.2f}”) print(f"Gradient Boosting 平均 AUC score: {auc:.2f}") print(“综合混淆矩阵：”) print(total_confusion_matrix) pipeline.fit(X_train, y_train) y_test_pred = pipeline.predict(X_test) y_test_proba = pipeline.predict_proba(X_test)[:, 1] accuracy_test = accuracy_score(y_test, y_test_pred) precision_test = precision_score(y_test, y_test_pred) recall_test = recall_score(y_test, y_test_pred) f1_test = f1_score(y_test, y_test_pred) auc_test = roc_auc_score(y_test, y_test_proba) print(f"测试集 accuracy: {accuracy_test:.2f}“) print(f"测试集 precision: {precision_test:.2f}”) print(f"测试集 recall: {recall_test:.2f}“) print(f"测试集 F1 score: {f1_test:.2f}”) print(f"测试集 AUC score: {auc_test:.2f}")

他们需要论文中的模型搭建部分，重点解释为什么选择GBDT而不是逻辑回归、SVM或随机森林，以及为什么使用管道和五折交叉验证。用户可能希望这部分内容结构清晰，有理论支持，并且结合他们的数据特点进行说明。接...

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导入多分类数据把混淆矩阵和AUC曲线画出来python代码

好的，以下是导入多分类数据并画出混淆矩阵和AUC曲线的Python代码： python # 导入必要的库 import numpy as np import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix, roc_curve, ...

data = pd.read_excel(‘C:/lydata/Traintest1.xlsx’) X = data.drop(‘HER2_G’, axis=1) y = data[‘HER2_G’] kf = KFold(n_splits=5, shuffle=True, random_state=42) accuracy_scores = [] precision_scores = [] recall_scores = [] f1_scores = [] auc_scores = [] total_confusion_matrix = np.zeros((len(np.unique(y)), len(np.unique(y))), dtype=int) rf = RandomForestClassifier(random_state=42, n_estimators=49, max_depth=4, class_weight=‘balanced’) rfe = RFE(rf, n_features_to_select=10) pipeline = Pipeline([ (‘smote’, SMOTE(k_neighbors=1,sampling_strategy=0.8, random_state=42)), (‘tomek’, TomekLinks()), (‘scaler’, StandardScaler()), (‘rfe’, rfe), (‘gb’, GradientBoostingClassifier( loss=‘log_loss’, learning_rate=0.03, n_estimators=1300, subsample=0.9, criterion=‘friedman_mse’, min_samples_split=2, min_samples_leaf=2, min_weight_fraction_leaf=0.0, max_depth=4, min_impurity_decrease=0.0, init=None, random_state=42, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=True, validation_fraction=0.1, n_iter_no_change=None, tol=0.0001, ccp_alpha=0.0 )) ]) for train_index, test_index in kf.split(X): X_train, X_test = X.iloc[train_index], X.iloc[test_index] y_train, y_test = y.iloc[train_index], y.iloc[test_index] pipeline.fit(X_train, y_train) y_pred = pipeline.predict(X_test) y_proba = pipeline.predict_proba(X_test)[:, 1] accuracy_scores.append(accuracy_score(y_test, y_pred)) precision_scores.append(precision_score(y_test, y_pred)) recall_scores.append(recall_score(y_test, y_pred)) f1_scores.append(f1_score(y_test, y_pred)) auc_scores.append(roc_auc_score(y_test, y_proba)) cm = confusion_matrix(y_test, y_pred) total_confusion_matrix += cm accuracy = np.mean(accuracy_scores) precision = np.mean(precision_scores) recall = np.mean(recall_scores) f1 = np.mean(f1_scores) auc = np.mean(auc_scores) print(“Gradient Boosting 参数：”) print(pipeline.named_steps[‘gb’].get_params()) print(f"Gradient Boosting 平均 accuracy: {accuracy:.2f}“) print(f"Gradient Boosting 平均 precision: {precision:.2f}”) print(f"Gradient Boosting 平均 recall: {recall:.2f}“) print(f"Gradient Boosting 平均 F1 score: {f1:.2f}”) print(f"Gradient Boosting 平均 AUC score: {auc:.2f}“) print(“综合混淆矩阵：”) print(total_confusion_matrix) pipeline.fit(X, y) test_data = pd.read_excel(‘C:/lydata/Testtest1.xlsx’) X_test = test_data.drop(‘HER2_G’, axis=1) y_test = test_data[‘HER2_G’] y_test_pred = pipeline.predict(X_test) y_test_proba = pipeline.predict_proba(X_test)[:, 1] accuracy_test = accuracy_score(y_test, y_test_pred) precision_test = precision_score(y_test, y_test_pred) recall_test = recall_score(y_test, y_test_pred) f1_test = f1_score(y_test, y_test_pred) auc_test = roc_auc_score(y_test, y_test_proba) print(f"测试集 accuracy: {accuracy_test:.2f}”) print(f"测试集 precision: {precision_test:.2f}“) print(f"测试集 recall: {recall_test:.2f}”) print(f"测试集 F1 score: {f1_test:.2f}“) print(f"测试集 AUC score: {auc_test:.2f}”) cm_test = confusion_matrix(y_test, y_test_pred) print(“测试集混淆矩阵：”)逐个分析每一句代码，并且分析每一个代码代码所执行后的操作有没有意义以及被正确使用

首先，用户提供的代码涉及数据加载、预处理、模型构建、交叉验证以及测试集评估等多个步骤。我需要仔细检查每一行代码，理解其作用，并判断是否存在潜在问题或可以优化的地方。首先，用户使用pandas读取Excel文件...

xgboost 五折交叉验证训练集AUC值代码

下面是一个使用 XGBoost 进行五折交叉验证并且计算训练集 AUC 值的示例代码： python import xgboost as xgb import numpy as np from sklearn.model_selection import cross_val_score # 假设 X 是特征数据，y...

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