在机器学习中，对模型的测量和评估同样至关重要。只有选择与问题相匹配的评估方法，我们才能够快速的发现在模型选择和训练过程中可能出现的问题，迭代地对模型进行优化。
常见的模型评估指标：
precision
recall
F1-score
PRC
ROC/AUC
IOU
(1)Next,i will introduce confusion matrix:
TP+FN+FP+FN=the whole sample
(2)PRC(Precision Recall Curve):[x:Recall,y:Precision]
(A>B>C)
(3)
F1 is a metric that consider both precision and recall,better than BEP.
(4)ROC and AUC
a.ROC
X:FPR,Y:TPR
TPR means Recall,FPR means sample predicted positive from actual neg.
AUC:the aera under ROC.If the classifer is enough good,the AUC=1,the higher AUC,the better classifier.
How to choose the evaluating metric?
(5)IOU:
附：
（1）precision with python
fT=file(r'C:\\Users\\hp\\Desktop\\trainlabel.txt','r')
fP=file(r'C:\\Users\\hp\\Desktop\\predictlabel.txt','r')
fTLines = fT.readlines();
fPLines = fP.readlines();
train_label = [0 for x in range(len(fTLines))]
predict_label = [0 for x in range(len(fPLines))]
count = 0
for eachline in fTLines:
eachline = eachline.strip('\n')
train_label[count] = eachline
count=count+1
count = 0
for eachline in fPLines:
eachline = eachline.strip('\n')
predict_label[count] = eachline
count=count+1
from sklearn import metrics
p=metrics.precision_score(train_label,predict_label,pos_label='1')
（2）roc/auc with python
import numpy as np
from sklearn.metrics import roc_curve
y = np.array([1,1,2,2])
pred = np.array([0.1,0.4,0.35,0.8])
fpr, tpr, thresholds = roc_curve(y, pred, pos_label=2)
print(fpr)
print(tpr)
print(thresholds)
from sklearn.metrics import auc
print(auc(fpr, tpr))
(3)recall with python
from sklearn.metrics import recall_score
y_true = [0, 1, 2, 0, 1, 2]
y_pred = [0, 2, 1, 0, 0, 1]
recall_score(y_true, y_pred, average='macro')
0.33...
recall_score(y_true, y_pred, average='micro')
0.33...
recall_score(y_true, y_pred, average='weighted')
0.33...
recall_score(y_true, y_pred, average=None)
array([1., 0., 0.])