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Merge pull request #344 from CoderOverflow/patch-3 

2.16.13 如何计算TPR，FPR？ 内容修订
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ch02_机器学习基础/第二章_机器学习基础.md
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@ -1182,7 +1182,6 @@ y_true = [0, 0, 1, 1]；scores = [0.1, 0.4, 0.35, 0.8]；
scores = [0.1, 0.4, 0.35, 0.8]；y_true = [0, 0, 1, 1]；y_pred = [1, 1, 1, 1]；
正例与反例信息如下：
| 真实值\预测值 | | |
| ------------- | ---- | ---- |
| | 正例 | 反例 |
| 正例 | TP=2 | FN=0 |
@ -1192,10 +1191,9 @@ scores = [0.1, 0.4, 0.35, 0.8]；y_true = [0, 0, 1, 1]；y_pred = [1, 1, 1, 1]
TPR = TP/(TP+FN) = 1； FPR = FP/(TN+FP) = 1；
当截断点为0.35时：
scores = [0.1, 0.4, 0.35, 0.8];y_true = [0, 0, 1, 1];y_pred = [0, 1, 1, 1];
scores = [0.1, 0.4, 0.35, 0.8]；y_true = [0, 0, 1, 1]；y_pred = [0, 1, 1, 1];
正例与反例信息如下：
| 真实值\预测值 | | |
| ------------- | ---- | ---- |
| | 正例 | 反例 |
| 正例 | TP=2 | FN=0 |
@ -1208,7 +1206,6 @@ TPR = TP/(TP+FN) = 1； FPR = FP/(TN+FP) = 0.5；
scores = [0.1, 0.4, 0.35, 0.8]；y_true = [0, 0, 1, 1]；y_pred = [0, 1, 0, 1]；
正例与反例信息如下：
| 真实值\预测值 | | |
| ------------- | ---- | ---- |
| | 正例 | 反例 |
| 正例 | TP=1 | FN=1 |
@ -1222,7 +1219,6 @@ scores = [0.1, 0.4, 0.35, 0.8]；y_true = [0, 0, 1, 1]；y_pred = [0, 0, 0, 1]
正例与反例信息如下：
| 真实值\预测值 | | |
| ------------- | ---- | ---- |
| | 正例 | 反例 |
| 正例 | TP=1 | FN=1 |
@ -1230,6 +1226,7 @@ scores = [0.1, 0.4, 0.35, 0.8]；y_true = [0, 0, 1, 1]；y_pred = [0, 0, 0, 1]
由此可得：
TPR = TP/(TP+FN) = 0.5； FPR = FP/(TN+FP) = 0；
4、根据TPR、FPR值，以FPR为横轴，TPR为纵轴画图。
### 2.16.14 如何计算AUC？