import matplotlib.pyplot as pltfrom sklearn import datasetsfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import label_binarizefrom sklearn.multiclass import OneVsRestClassifierfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import roc_curve, aucfrom ...
from sklearn import svm, datasets from sklearn.metrics import roc_curve, auc from sklearn.model_selection import train_test_split from sklearn.preprocessing import label_binarize from sklearn.multiclass import OneVsRestClassifier from scipy import interp # 导入数据 iris = datasets.load_iris() X ...
from sklearn import svm, datasets from sklearn.metrics import roc_curve, auc from sklearn.model_selection import train_test_split from sklearn.preprocessing import label_binarize from sklearn.multiclass import OneVsRestClassifier from scipy import interp # 加载数据 iris = datasets.load_iris() x ...
from sklearn import svm, datasets from sklearn.metrics import roc_curve, auc from sklearn.model_selection import train_test_split from sklearn.preprocessing import label_binarize from sklearn.multiclass import OneVsRestClassifier from scipy import interp # 加载数据 iris = datasets.load_iris() X ...
python sklearn 也可以绘制多分类 roc, 具体见Multiclass Receiver Operating Characteristic (ROC) - scikit-learn. 其他 ROC 与 PR-Curve 的比较6 样本比例变化时的 ROC 曲线性能变化情况 这里就要讨论到 roc 至于其他指标的一个优势了,他不受正负样本比例的影响,可以在比例极其不均的样本上得到优秀的表达效果。
在Python中,我们可以使用scikit-learn库来实现多类别分类的ROC。以下是使用scikit-learn库进行多类别分类ROC的示例代码: fromsklearn.metricsimportroc_curve,aucfromsklearn.preprocessingimportlabel_binarizefromsklearn.multiclassimportOneVsRestClassifierfromsklearn.model_selectionimporttrain_test_splitfromsklearn.svmimp...
from sklearn.metrics import roc_curve from sklearn.model_selection import train_test_split from sklearn.preprocessing import label_binarize from sklearn.multiclass import OneVsRestClassifier from scipy import interp #导入各种包,作用下面会说
from sklearn.metricsimportroc_auc_score y_true=np.array([0,0,1,1])y_scores=np.array([0.1,0.4,0.35,0.8])roc_auc_score(y_true,y_scores) 2、ROC曲线 代码语言:javascript 复制 y=np.array([1,1,2,2])scores=np.array([0.1,0.4,0.35,0.8])fpr,tpr,thresholds=roc_curve(y,scores,pos_lab...
from sklearn import svm, datasets from sklearn.metrics import roc_curve, auc from sklearn.model_selection import train_test_split from sklearn.preprocessing import label_binarize from sklearn.multiclass import OneVsRestClassifier from scipy import interp ...
from sklearn import svm, datasets from sklearn.metrics import roc_curve, auc from sklearn.model_selection import train_test_split fromsklearn.preprocessing import label_binarize from sklearn.multiclass import OneVsRestClassifier from scipy import interp ...