from sklearn.metrics import roc_auc_score 文心快码BaiduComate 基于你的问题和提供的tips,以下是对如何使用roc_auc_score函数的详细解答: 1. 导入roc_auc_score函数 首先,我们需要从sklearn.metrics模块中导入roc_auc_score函数。这是计算ROC曲线下面积(AUC)的关键函数,通常用于评估二分类模型的性能。 python ...
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...
auc = roc_auc_score(ground_truth, pred)print()print(f"Validation AUC:{auc:.4f}") 准确率达93.05%
import numpy as np from sklearn.model_selection import RepeatedKFold from sklearn.cross_decomposition import PLSRegression from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.ensemble import RandomForestClassifier,VotingClassifier from sklearn.metrics import roc_auc_score,make_sc...
from typing import Any, Callable, Tuple import torch from ignite.metrics import EpochMetric def roc_auc_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> float: try: from sklearn.metrics import roc_auc_score except ImportError: ...
)# roc curve and auc scorefromsklearn.datasetsimportmake_classificationfromsklearn.neighborsimportKNeighborsClassifierfromsklearn.ensembleimportRandomForestClassifierfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportroc_curvefromsklearn.metricsimportroc_auc_scoredefplot_roc_curve(fpr, ...
plot the ROC curve for SMS spam importnumpyasnpimportpandasaspdimportmatplotlib.pyplotaspltfromsklearn.feature_extraction.textimportTfidfVectorizerfromsklearn.linear_model.logisticimportLogisticRegressionfromsklearn.cross_validationimporttrain_test_split,cross_val_scorefromsklearn.metricsimportroc_curve,auc ...
# 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...
as_matrix()) from sklearn.linear_model import LogisticRegression from sklearn.ensemble import RandomForestClassifier, VotingClassifier from sklearn.model_selection import StratifiedKFold from sklearn.metrics import precision_score, recall_score, confusion_matrix, classification_report, roc_curve, precision...
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...