[Machine Learning]---ROC(Receiver Operating Characteristic) Curve

1. Some Basic Concepts

• True Positive Rate:
  
  TPR=TPTP+FN
• False Positive Rate
  
  FPR=FPFP+TN

2. ROC Curve

ROC use the TPR as the y axis and FPR as the x axis, so we hope that y is as large as possible and x is as small as possible.
In a picture we hope that the ROC cure in the high and left.
So, how to choose the threshold θ.
1. if h(x)>θ, we classify the x is Positive as 1.
2. If h(x)<\theat, we classify the x as Negative as 0.
Let’s plot the picture.

3. Reference

http://blog.csdn.net/abcjennifer/article/details/7359370