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b/docs/misc/linear-logistic-mle.md similarity index 100% rename from docs/linear-logistic-mle.md rename to docs/misc/linear-logistic-mle.md diff --git a/docs/naive-bayes-discuss.md b/docs/misc/naive-bayes-discuss.md similarity index 98% rename from docs/naive-bayes-discuss.md rename to docs/misc/naive-bayes-discuss.md index 3d063a81..d973c671 100644 --- a/docs/naive-bayes-discuss.md +++ b/docs/misc/naive-bayes-discuss.md @@ -1,32 +1,32 @@ - -## 朴素贝叶斯讨论 - -> [@Time渐行渐远](https://github.com/Timehsw) [@那伊抹微笑](https://github.com/wangyangting) [@小瑶](https://github.com/chenyyx) [@如果迎着风就飞](https://github.com/orgs/apachecn/people/mikechengwei) - -朴素贝叶斯就是用来求逆向概率的（已知）。 - -1. 根据训练数据集求（正向）概率。 -2. 根据测试数据集求（逆向）概率（根据 贝叶斯公式）。 -3. 求出的逆向概率，哪个大，就属于哪个类别。 - -### 疑问 1 -通过训练集求出了各个特征的概率, 然后测试集的特征和之前求出来的概率相乘, 这个就代表这个测试集的特征的概率了. -有了这个基础后, 通过贝叶斯公式, 就可以得到这个测试集的特征属于哪个类别了, 他们相乘的依据是什么？ - -``` -朴素贝叶斯？ -条件独立性啊 -朴素贝叶斯不是基于两个定理吗 -一个是假设 条件独立性 -一个是 贝叶斯定理 -条件独立性 所以每个特征相乘得到的概率 就是这个数据的概率 -``` - -### 疑问 2 -凭啥测试集的特征乘以训练集的概率就是测试集的概率了.这么做的理论依据是什么？ - -``` -朴素贝叶斯就是利用先验知识来解决后验概率，因为训练集中我们已经知道了每个单词在类别0和1中的概率，即p(w|c). -我们就是要利用这个知识去解决在出现这些单词的组合情况下，类别更可能是0还是1,即p(c|w). -如果说之前的训练样本少, 那么这个 p(w|c) 就更可能不准确, 所以样本越多我们会觉得这个 p(w|c) 越可信. -``` + +## 朴素贝叶斯讨论 + +> [@Time渐行渐远](https://github.com/Timehsw) [@那伊抹微笑](https://github.com/wangyangting) [@小瑶](https://github.com/chenyyx) [@如果迎着风就飞](https://github.com/orgs/apachecn/people/mikechengwei) + +朴素贝叶斯就是用来求逆向概率的（已知）。 + +1. 根据训练数据集求（正向）概率。 +2. 根据测试数据集求（逆向）概率（根据 贝叶斯公式）。 +3. 求出的逆向概率，哪个大，就属于哪个类别。 + +### 疑问 1 +通过训练集求出了各个特征的概率, 然后测试集的特征和之前求出来的概率相乘, 这个就代表这个测试集的特征的概率了. +有了这个基础后, 通过贝叶斯公式, 就可以得到这个测试集的特征属于哪个类别了, 他们相乘的依据是什么？ + +``` +朴素贝叶斯？ +条件独立性啊 +朴素贝叶斯不是基于两个定理吗 +一个是假设 条件独立性 +一个是 贝叶斯定理 +条件独立性 所以每个特征相乘得到的概率 就是这个数据的概率 +``` + +### 疑问 2 +凭啥测试集的特征乘以训练集的概率就是测试集的概率了.这么做的理论依据是什么？ + +``` +朴素贝叶斯就是利用先验知识来解决后验概率，因为训练集中我们已经知道了每个单词在类别0和1中的概率，即p(w|c). +我们就是要利用这个知识去解决在出现这些单词的组合情况下，类别更可能是0还是1,即p(c|w). +如果说之前的训练样本少, 那么这个 p(w|c) 就更可能不准确, 所以样本越多我们会觉得这个 p(w|c) 越可信. +``` diff --git 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