from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
y_true = [1,0,0]
y_predict = [.6,.1,.1]
fpr, tpr, thresholds = metrics.roc_curve(y_true, y_predict , pos_label=1)
print(fpr)
print(tpr)
print(thresholds)
# Print ROC curve
plt.plot(fpr,tpr)
plt.show()
y_true = [1,0,0]
y_predict = [.6,.1,.6]
fpr, tpr, thresholds = metrics.roc_curve(y_true, y_predict , pos_label=1)
print(fpr)
print(tpr)
print(thresholds)
# Print ROC curve
plt.plot(fpr,tpr)
plt.show()
例如,对于值:
y_true = [1,0,0]
y_predict = [.6,.1,.6]
返回以下阈值:
[1.6 0.6 0.1]
更新:
从
https://sachinkalsi.github.io/blog/category/ml/2018/08/20/top-8-performance-metrics-one-should-know.html#receiver-operating-characteristic-curve-roc
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
y_true = [1,1,1,0]
y_predict = [.94,.87,.83,.80]
fpr, tpr, thresholds = metrics.roc_curve(y_true, y_predict , pos_label=1)
print('false positive rate:', fpr)
print('true positive rate:', tpr)
print('thresholds:', thresholds)
# Print ROC curve
plt.plot(fpr,tpr)
plt.show()
它产生了这个图:
绘图与博客中引用的绘图不同,阈值也不同:
另外,使用scikit返回的阈值
metrics.roc_curve
实施内容包括:
thresholds: [0.94 0.83 0.8 ]
. scikit是否应该返回与使用相同点时相似的roc曲线?我应该自己实现roc曲线,而不是依赖scikit实现,因为结果是不同的?
