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 from sklearn.metrics import roc_auc_score 2. 准备真实标签和预测概率 在使用roc_auc_score函数之前,我们需要准备两组数据:真实标签(y_true)和预测概率(y_score)。
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# 博客:http...
Embedding(data["movie"].num_nodes, hidden_channels)#movie用embedding映射成64维向量,后续加上全连接的64维向量,做得更好# Instantiate homogeneous GNN:self.gnn = GNN(hidden_channels)#同构图# Convert GNN model into a heterogeneous variant:self.gnn = to_hetero(self.gnn, metadata=data.metadata())#...
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...
importnumpyasnpimportpandasaspdimportmatplotlib.pyplotaspltfromsklearn.feature_extraction.textimportTfidfVectorizerfromsklearn.linear_model.logisticimportLogisticRegressionfromsklearn.cross_validationimporttrain_test_split,cross_val_scorefromsklearn.metricsimportroc_curve,auc ...
import click import numpy as np import pandas as pd from sklearn.metrics import get_scorer_names from clep.classification import do_classification from clep.embedding import ( from .classification import do_classification from .embedding import ( do_ss_evaluation, do_graph_gen, do_kge) from clep...
# 需要导入模块: from sklearn.feature_selection import SelectFromModel [as 别名]# 或者: from sklearn.feature_selection.SelectFromModel importtransform[as 别名]deffeature_selection(model, X_train, X_test, y_train, y_test, eval_metric='auc'):thresholds = [thresforthresinsorted(model.feature_imp...
importnumpyasnpimportpandasaspdimportscipyascpfromscipyimportstatsimportmatplotlib.pyplotaspltimportseabornassnsfromsklearnimportpreprocessingfromsklearn.metricsimportconfusion_matrix,roc_auc_scorefromsklearn.model_selectionimportStratifiedKFold,cross_val_score,KFoldfromxgboostimportXGBClassifierimportxgboostasxgbimport...
# 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...
from sklearn.metrics import recall_score recall_score(label, prediction) F1 ScoreThe f1 score is the harmonic mean(调和平均) of recall and precision, with a higher score as a better model.F 1=\frac{2}{\frac{1}{\text { precision }}+\frac{1}{\text { recall }}}=\frac{2 * \text...