形式: from sklearn.metrics import roc_auc_score auc_score = roc_auc_score(y_test,y_pred) 说明: y_pred即可以是类别,也可以是概率。 roc_auc_score直接根据真实值和预测值计算auc值,省略计算roc的过程。 1# -*- coding: utf-8 -*-2"""3# 作者:wanglei52054# 邮箱:wanglei5205@126.com5# 博...
python from sklearn.metrics import roc_curve 运行该导入语句,确保没有错误: 在导入roc_curve后,你的代码应该能够正常运行,没有导入错误。如何使用roc_curve函数 如果你想要了解如何使用roc_curve函数,以下是更详细的步骤: 了解roc_curve函数的基本用法和参数: roc_curve函数用于计算接收者操作特征(ROC)曲线的...
If we change this threshold the performance metrics will change It would be nice to be able to evaluate the performance of a model without the need to select an arbitrary threshold. This is precisely what AUC-ROC is providing.Area Under Curve (AUC)--Area Under the curve of the Receiver ...
display:RocCurveDisplay ROC 曲线显示。 例子: >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import make_classification >>> from sklearn.metrics import RocCurveDisplay >>> from sklearn.model_selection import train_test_split >>> from sklearn.svm import SVC >>> X, y = mak...
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...
import sklearn.metrics as metrics import matplotlib.pyplot as plt fpr, tpr, th = metrics.roc_curve(test.bad_good, lg_m1.predict(test)) plt.figure(figsize=[6, 6]) plt.plot(fpr, tpr, 'b--') plt.title('ROC curve') plt.show() ...
# 引入随机森林 from sklearn.ensemble import RandomForestRegressor # 引入ROC/AUC Scroe from sklearn.metrics import roc_auc_score import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns sns.set_style('whitegrid') %matplotlib inline 1. 2. 3. 4. 5. 6...
importnumpyasnpimportpandasaspdimportmatplotlib.pyplotaspltfromsklearn.feature_extraction.textimportTfidfVectorizerfromsklearn.linear_model.logisticimportLogisticRegressionfromsklearn.cross_validationimporttrain_test_split,cross_val_scorefromsklearn.metricsimportroc_curve,auc ...
好了, 接下来当sklearn调包侠,并且分出训练集测试集合,并对amount字段作标准化处理后作图 fromsklearn.model_selectionimporttrain_test_splittrain_set,test_set=train_test_split(data,test_size=0.1,random_state=42)data=train_setvalid_data=test_setfromsklearn.preprocessingimportStandardScalerdata['normAmount...
from sklearn.ensemble import RandomForestRegressor # 引入ROC/AUC Scroe from sklearn.metrics import roc_auc_score import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns sns.set_style('whitegrid') %matplotlib inline然后我们把数据集加载到项目中1...