Comments (4)
同学你好,书上说的是“将分类阈值依次设为每个样例的预测值”,并不是“按顺序逐个把样本作为正例”。
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不是的,书上说的是“我们根据机器学习的预测结果对样本进行排序,按此顺序逐个把样本作为正例预测,每次计算……”(西瓜书33页倒数第一段)
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@zhanghexie 同学你好,33页那句话是不能严格扣字面意思的,那里我觉得是周老师进行一个简单的概述而已,不能作为画ROC曲线的标准规范,那句话要理解也应该理解为“按顺序逐个把样本的预测值作为分类阈值来判别正例”,也就是和34页的倒数第三段的倒数第四句话“将分类阈值依次设为每个样例的预测值”是同一个意思,因为画ROC曲线的标准规范就是34页的倒数第三段给出的,同时我们对公式2.21的解析也是严格基于这一段话的。
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@Sm1les 好的,知道了,多谢大佬指点。
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Related Issues (20)
- bolzmann 机 公式5.20 的下标错误 HOT 1
- 在线的链接无法打开 HOT 1
- ROC曲线为什么真正例率与假正例率可以同时增加 HOT 2
- 机器学习
- 南瓜书中的公式2.21的讲解感觉有点晦涩了 HOT 1
- 公式(12.39)的解释有问题 HOT 2
- 公式12.36
- released版本的pdf中有一处错误
- 西瓜書
- 书中 公式(3.35) 公式(3.36)怎么没有啊! HOT 1
- 公式16.16 HOT 2
- 公式16.16
- 进不去链接 HOT 5
- 式 2.27 HOT 2
- 10.17 求解CPA时的问题
- 关于公式3-9
- @yanglei-github 事件{f(x)=1}和{f(x)=-1}已经是完备事件组了,求期望是 权重(概率)*值,然后P(f(x)=1|x)是概率,e^(-H(x)f(x))为值,又知道f(x)=1,那么值就是e^(-H(x)),然后把P(f(x)=-1|x)也加上就出现了上述式子
- 第五章 式(5.2) 中最后推导梯度的时候,\hat(yi)也应该是关于w的函数,没有对其求导,直接当成常数处理了,这似乎是不正确的。 HOT 1
- > @wanyixue 同学你好,损失函数L是关于w和theta的函数,只有w和theta是未知的变量,\hat(yi)和yi都是已知量,所以不用对他们求导,因此也不存在不可导一说
- There are no page numbers in the most recent PDF file pages HOT 4
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