October 2021
Intermediate to advanced
289 pages
8h 31m
Chinese
Content preview from 数据科学中的实用统计学(第2版)
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分类
｜
185
除了
ROC
曲线，研究精确度
–
召回率（
PR
）曲线也能给我们很多启示。
PR
曲线的计算方法与
ROC
曲
线非常类似，只是它的数据是按照可能为
1
的概
率从小到大排序的，而且计算的是累积精确度和召回率。在评估结果非常不
平衡的数据时，
PR
曲线尤其有用。
5.4.5
AUC
ROC
曲线是一种非常有价值的图形工具，但它本身并不能被视为对分类器性能的一种量
度。不过，
ROC
曲线可以生成曲线下面积
（
area underneath the curve
，
AUC
）这个指标。
AUC
就是
ROC
曲线下的总面积
。
AUC
的值越大，分类器就越有效。完美分类器的
AUC
为
1
，即它可以对所有的
1
进行正确分类，而不会将任何
0
误分类为
1
。
完全无效的分类器（对角线）的
AUC
是
0.5
。
图
5-7
给出了贷款模型
ROC
曲线下的面积。
AUC
的值可以通过
R
的数值积分来计算：
sum(roc_df$recall[-1] * diff(1 - roc_df$specificity))
[1] 0.6926172
在
Python
中，可以像在
R
中那样计算准确率，也可以使用
scikit-learn
中的
sklearn.
metrics.roc_auc_score
函数，这时需要你确定期望的值是
0
还是
1
：
print(np.sum(roc_df.recall[:-1] * np.diff(1 - roc_df.specificity)))
print(roc_auc_score([1 if yi == 'default' else 0 for yi in y], ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.