RocCurveDisplay是scikit-learn库中的一个类,用于可视化 ROC 曲线(Receiver Operating Characteristic curve)。如果你无法从sklearn.metrics导入RocCurveDisplay,可能是以下几个原因: 原因分析 版本问题:RocCurveDisplay可能在你安装的scikit-learn版本中不可用。这个类是在较新的版本中引入的。
对于sklearn函数的错误消息“'RocCurveDisplay‘没有属性'from_predictions’”,这个错误消息表明在使用RocCurveDisplay函数时发生了问题。具体来说,该函数没有名为'from_predictions'的属性。 要解决这个问题,可以采取以下步骤: 确认sklearn的版本:首先,确保你正在使用最新版本的sklearn库。可以通过在...
print(cohen_kappa_score(y_test, predict_target)) 7 绘制ROC曲线,计算AUC值 # 绘制ROC曲线,计算AUC值 from sklearn.metrics import RocCurveDisplay # 使用 RocCurveDisplay.from_estimator 绘制 ROC 曲线 RocCurveDisplay.from_estimator(model,X_test, y_test) plt.plot([0, 1], [0, 1], linestyle='...
fromsklearn.metricsimportroc_curvefromsklearn.metricsimportRocCurveDisplay fpr,tpr,thresholds=metrics.roc_curve(y,pred,pos_label=1)roc_display=RocCurveDisplay(fpr=fpr,tpr=tpr,roc_auc=roc_auc,estimator_name='example estimator').plot(color="darkorange")roc_display.roc_auc# 0.8366666666666667 自定义...
例如ROC 曲线。 The returnedsvc_dispobject allows us to continue using the already computed ROC curve for SVC in future plots. In this case, thesvc_dispis aRocCurveDisplaythat stores the computed values as attributes calledroc_auc,fpr, andtpr. Next, we train a random forest classifier and ...
(y_test,predict_target))#8 绘制ROC曲线,计算AUC值fromsklearn.metricsimportRocCurveDisplay# 使用 RocCurveDisplay.from_estimator 绘制 ROC 曲线RocCurveDisplay.from_estimator(model,X_test,y_test)plt.plot([0,1],[0,1],linestyle='--',color='gray')plt.xlabel('False Positive Rate')plt.ylabel('...
roc_auc[i] = auc(fpr[i], tpr[i]) # Compute micro-average ROC curve and ROC area fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel()) roc_auc["micro"] = auc(fpr["micro"], tpr["micro"]) plt.figure() lw = 2 plt.plot(fpr[2], tpr[2], colo...
from sklearn.metrics import roc_curve # 编码库 from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import OneHotEncoder # 交叉验证 from sklearn.model_selection import cross_val_score 1. 2. 3. 4. 5. 6. 7. 8.
import numpy as npimport pandas as pd# 导入SVC模型from sklearn.svm import SVC# 导入评分指标from sklearn.metrics import accuracy_scorefrom sklearn.metrics import roc_auc_scorefrom sklearn.metrics import roc_curve# 编码库from sklearn.preprocessing import LabelEncoderfrom sklearn.preprocessing import ...
sklearn.metrics.plot_roc_curve fromsklearn.model_selectionimporttrain_test_split fromsklearn.svmimportSVC fromsklearn.metricsimportplot_roc_curve fromsklearn.datasetsimportload_wine X, y = load_wine(return_X_y=True) y = y ==2 X_train, X_test...