isLouisHsu / isLouisHsu/isLouisHsu.github.io
TF-IDF | LOUIS' BLOG
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- HTML
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Description
https://louishsu.xyz/2018/10/25/TF-IDF/
引言正在做LintCode上的垃圾邮件分类，使用朴素贝叶斯方法解决，涉及到文本特征的提取。TF-IDF（词频-逆文档频率）算法是一种统计方法，用以评估一字词对于一个文件集或一个语料库中的其中一份文件的重要程度。字词的重要性随着它在文件中出现的次数成正比增加，但同时会随着它在语料库中出现的频率成反比下降。 计算步骤词频(TF)Term Frequency，就是某个关键字出现的频率，具体来讲，就是词库
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue links to a TF-IDF blog article and includes article text, but names no source file, test, or requested change. First clarify whether the article should be added, revised, or otherwise handled; completion cannot be defined from the current issue alone.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- html
- Domain
- content
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100