您好我正在尝试使用以下数据绘制召回精度曲线：
Recall Precision
0.88196 0.467257
0.898501 0.468447
0.89899 0.470659
0.900789 0.471653
0.900922 0.472038
0.901012 0.472359
0.901345 0.480144
0.901695 0.482353
0.902825 0.482717
0.903261 0.483125
0.905152 0.483621
0.905575 0.485088
0.905682 0.486339
0.906109 0.488117
0.906466 0.488459
0.90724 0.488587
0.908989 0.488875
0.909941 0.489362
0.910125 0.489493
0.910314 0.490196
0.910989 0.49022
0.91106 0.490786
0.911137 0.496624
0.91129 0.496891
0.911392 0.497301
0.911392 0.499379
0.911422 0.5
0.911452 0.503783
0.911525 0.515829
源代码：
import random
import pylab as pl
from sklearn import svm, datasets
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import auc
##Load Recall
fname = "recall.txt"
fname1 = "precision.txt"
recall = []
precision = []
with open(fname) as inf:
for line in inf:
recall.append(float(line))
with open(fname1) as inf:
for line in inf:
precision.append(float(line))
area = auc(recall, precision)
print("Area Under Curve: %0.2f" % area)
pl.clf()
pl.plot(recall, precision, label='Precision-Recall curve')
pl.xlabel('Recall')
pl.ylabel('Precision')
pl.ylim([0.0, 1.05])
pl.xlim([0.0, 1.0])
pl.title('Precision-Recall example: AUC=%0.2f' % area)
pl.legend(loc="lower left")
pl.show()
我得到 AUC = 0.01 下的面积是正常的吗？
