使用RocCurveDisplay来计算和绘制ROC曲线。 # 计算ROC曲线fpr,tpr,thresholds=roc_curve(y_test,y_scores)roc_auc=roc_auc_score(y_test,y_scores)# 绘制ROC曲线plt.figure(figsize=(10,6))plt.plot(fpr,tpr,label='ROC curve (area = %0.2f)'%ro
本文简要介绍python语言中 sklearn.metrics.RocCurveDisplay 的用法。 用法: class sklearn.metrics.RocCurveDisplay(*, fpr, tpr, roc_auc=None, estimator_name=None, pos_label=None) ROC 曲线可视化。 建议使用 from_estimator 或from_predictions 创建RocCurveDisplay 。所有参数都存储为属性。 在用户指南中...
存储计算值的对象。 例子: >>>importmatplotlib.pyplotasplt>>>fromsklearn.datasetsimportmake_classification>>>fromsklearn.metricsimportRocCurveDisplay>>>fromsklearn.model_selectionimporttrain_test_split>>>fromsklearn.svmimportSVC>>>X, y = make_classification(random_state=0)>>>X_train, X_test, y...
在Python中绘制混淆矩阵和ROC曲线,我们需要导入scikit-learn和matplotlib库: import numpy as np import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, roc_curve, auc from sklearn.model_selection import train_test_split from sklearn.datasets import load_iris ...
fromsklearn.calibrationimportcalibration_curve fromsklearn.metricsimport(roc_curve, auc, confusion_matrix, ConfusionMatrixDisplay, RocCurveDisplay, precision_recall_curve, precision_score) fromsklearn.ensembleimport(RandomForestClassi...
from sklearn.metrics import (roc_curve,auc,confusion_matrix, ConfusionMatrixDisplay,RocCurveDisplay,precision_recall_curve,precision_score) from sklearn.ensemble import (RandomForestClassifier, GradientBoostingClassifier) from xgboost import XGBClassifier ...
使用matplotlib和sklearn.metrics.RocCurveDisplay来绘制ROC曲线。 python # 绘制ROC曲线 roc_display = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name='Logistic Regression') roc_display.plot() # 显示图形 plt.show() 总结 通过以上步骤,我们成功地在Python中实现了逻辑回归模型,并绘...
二分类问题可以画出ROC曲线,计算 对应的AUC值: from sklearn.metrics import RocCurveDisplay model=MultinomialNB() model.fit(X_train, y_train) RocCurveDisplay.from_estimator(model, X_test, y_test) x = np.linspace(0, 1, 100) plt.plot(x, x, 'k--', linewidth=1) ...
#进行ROC曲线绘制计算准备 # у得分为模型预测正例的概率 y_score =nnmodel.predict_proba(X_test)[:,1] #计算不同阈值下,fpr和tpr的组合值,其中fpr表示1-Specificity,tpr表示sensitivity fpr,tpr,threshold =metrics.roc_curve(y_test,y_score) # 计算AUC的值 roc_auc = metrics.auc(fpr,tpr) print("...
RocCurveDisplay.from_predictions( y_onehot_test[:, class_id], y_test_prob[:, class_id], name=f"ROC curve for {target_names[class_id]}", color=color, ax=ax, ) plt.plot([0, 1], [0, 1], "k--", label="ROC curve for chance level (AUC = 0.5)") ...