# 机器学习::文本特征提取（TF-IDF） - 第二部分

Read the first part of this tutorial:Text feature extraction (tf-idf) – Part I

Since a lot of people liked the first part of this tutorial, this second part is a little longer than the first.

### 介绍

In the first post, we learned how to use theterm-frequency以表示在矢量空间的文本信息。然而，与术语频率方法的主要问题是，它大大加快了频繁的条款和规模下降，这比高频方面经验更丰富罕见的条款。基本的直觉是，在许多文件中经常出现的一个术语不太好鉴别，真正有意义的（至少在许多实验测试）;这里最重要的问题是：你为什么会在例如分类问题，强调术语，是在你的文档的整个语料库几乎礼物？

But let’s go back to our definition of the$\ mathrm {TF}（T，d）$which is actually the term count of the term$t$在文档中$d$。The use of this simple term frequency could lead us to problems likekeyword spamming, which is when we have a repeated term in a document with the purpose of improving its ranking on an IR (信息检索）系统，甚至对创建长文档偏见，使他们看起来比他们只是因为手册中出现的高频更重要。

To overcome this problem, the term frequency$\ mathrm {TF}（T，d）$of a document on a vector space is usually also normalized. Let’s see how we normalize this vector.

### 矢量归

Suppose we are going to normalize the term-frequency vector$\ {VEC V_ {D_4}}$我们在本教程的第一部分已经计算。该文件$d4$from the first part of this tutorial had this textual representation:

D4：我们可以看到闪亮的阳光，明亮的阳光下。

And the vector space representation using the non-normalized term-frequency of that document was:

$\ {VEC V_ {D_4}} =（0,2,1,0）$

$\ displaystyle \帽子{v} = \压裂vec {v}} {\ vec {v} {\ | \ \ |_p}$

Where the$\帽子{V}$是单位矢量，或者归一化矢量，所述$\ VEC {V}$在矢量将被归一化和$\ | \ VEC {V} \ | _p$是矢量的范数（大小，长度）$\ VEC {V}$in the$L^p$空间（别担心，我将所有的解释）。

The unit vector is actually nothing more than a normalized version of the vector, is a vector which the length is 1.

### Lebesgue spaces

Usually, the length of a vector$\ {VEC U】=（U_1，U_2，U_3，\ ldots，u_n）$is calculated using the欧几里得范一个准则是在矢量空间中分配一个严格正长度或大小于所有矢量的函数-, which is defined by:

$\|\vec{u}\| = \sqrt{u^2_1 + u^2_2 + u^2_3 + \ldots + u^2_n}$

$\displaystyle \|\vec{u}\|_p = ( \left|u_1\right|^p + \left|u_2\right|^p + \left|u_3\right|^p + \ldots + \left|u_n\right|^p )^\frac{1}{p}$

$\的DisplayStyle \ | \ VEC【U} \ | _p =（\总和\ limits_ {I = 1} ^ {N} \左| \ VEC {U】_i \右| ^ P）^ \压裂{1} {P}$

$\的DisplayStyle \ | \ VEC【U} \ | _1 =（\左| U_1 \右| + \左| U_2 \右| + \左| U_3 \右| + \ ldots + \左| u_n \右|）$

Taxicab geometry versus Euclidean distance: In taxicab geometry all three pictured lines have the same length (12) for the same route. In Euclidean geometry, the green line has length$6 \倍\ SQRT {2} \约8.48$，并且是唯一的最短路径。

### Back to vector normalization

$\帽子{V}= \frac{\vec{v}}{\|\vec{v}\|_p} \\ \\ \hat{v_{d_4}} = \frac{\vec{v_{d_4}}}{||\vec{v_{d_4}}||_2} \\ \\ \\ \hat{v_{d_4}} = \frac{(0,2,1,0)}{\sqrt{0^2 + 2^2 + 1^2 + 0^2}} \\ \\ \hat{v_{d_4}} = \frac{(0,2,1,0)}{\sqrt{5}} \\ \\ \small \hat{v_{d_4}} = (0.0, 0.89442719, 0.4472136, 0.0)$

Note that here we have normalized our term frequency document vector, but later we’re going to do that after the calculation of the tf-idf.

### 术语频率 - 逆文档频率（TF-IDF）重量

火车文档集：D1：天空是蓝色的。D2：阳光灿烂。测试文档集：D3：在天空，阳光灿烂。D4：我们可以看到闪亮的阳光，明亮的阳光下。

Your document space can be defined then as$d = \ {D_1，D_2，\ ldots，D_N \}$哪里$n$是个number of documents in your corpus, and in our case as$D_ {火车} = \ {D_1，D_2 \}$$D_ {测试} = \ {D_3，D_4 \}$。The cardinality of our document space is defined by$\left|{D_{train}}\right| = 2$$\左| {{D_测试}} \右|= 2$, since we have only 2 two documents for training and testing, but they obviously don’t need to have the same cardinality.

$\的DisplayStyle \ mathrm {IDF}（T）= \日志{\压裂{\左| d \右|} {1+ \左| \ {d：吨\在d \} \右|}}$

$\ mathrm {TF \ MBOX { - } IDF}（T）= \ mathrm {TF}（T，d）\倍\ mathrm {IDF}（t）的$

Now let’s calculate the idf for each feature present in the feature matrix with the term frequency we have calculated in the first tutorial:

$M_ {}列车= \begin{bmatrix} 0 & 1 & 1 & 1\\ 0 & 2 & 1 & 0 \end{bmatrix}$

Since we have 4 features, we have to calculate$\ mathrm {IDF}（T_1）$,$\ mathrm {IDF}（T_2）$,$\ mathrm {IDF}（t_3处）$,$\mathrm{idf}(t_4)$:

$\ mathrm {IDF}（T_1）= \日志{\压裂{\左| d \右|} {1+ \左| \ {d：T_1 \在d \} \右|}} = \日志{\压裂{2} {1}} = 0.69314718$

$\ mathrm {IDF}（T_2）= \log{\frac{\left|D\right|}{1+\left|\{d : t_2 \in d\}\right|}} = \log{\frac{2}{3}} = -0.40546511$

$\ mathrm {IDF}（t_3处）= \日志{\压裂{\左| d \右|} {1+ \左| \ {d：t_3处\在d \} \右|}} = \日志{\压裂{2} {3}} = -0.40546511$

$\ mathrm {IDF}（T_4）= \日志{\压裂{\左| d \右|} {1+ \左| \ {d：T_4 \在d \} \右|}} = \日志{\压裂{2} {2}} = 0.0$

$\ {VEC {idf_列车}}= (0.69314718, -0.40546511, -0.40546511, 0.0)$

$M_ {} IDF= \begin{bmatrix} 0.69314718 & 0 & 0 & 0\\ 0 & -0.40546511 & 0 & 0\\ 0 & 0 & -0.40546511 & 0\\ 0 & 0 & 0 & 0 \end{bmatrix}$

$M_{tf\mbox{-}idf} = M_{train} \times M_{idf}$

Please note that the matrix multiplication isn’t commutative, the result of$A \乘以B$will be different than the result of the$乙\一个时代$，这就是为什么$M_ {} IDF$is on the right side of the multiplication, to accomplish the desired effect of multiplying each idf value to its corresponding feature:

$\begin{bmatrix} \mathrm{tf}(t_1, d_1) & \mathrm{tf}(t_2, d_1) & \mathrm{tf}(t_3, d_1) & \mathrm{tf}(t_4, d_1)\\ \mathrm{tf}(t_1, d_2) & \mathrm{tf}(t_2, d_2) & \mathrm{tf}(t_3, d_2) & \mathrm{tf}(t_4, d_2) \end{bmatrix} \times \begin{bmatrix} \mathrm{idf}(t_1) & 0 & 0 & 0\\ 0 & \mathrm{idf}(t_2) & 0 & 0\\ 0 & 0 & \mathrm{idf}(t_3) & 0\\ 0 & 0 & 0 & \mathrm{idf}(t_4) \end{bmatrix} \\ = \begin{bmatrix} \mathrm{tf}(t_1, d_1) \times \mathrm{idf}(t_1) & \mathrm{tf}(t_2, d_1) \times \mathrm{idf}(t_2) & \mathrm{tf}(t_3, d_1) \times \mathrm{idf}(t_3) & \mathrm{tf}(t_4, d_1) \times \mathrm{idf}(t_4)\\ \mathrm{tf}(t_1, d_2) \times \mathrm{idf}(t_1) & \mathrm{tf}(t_2, d_2) \times \mathrm{idf}(t_2) & \mathrm{tf}(t_3, d_2) \times \mathrm{idf}(t_3) & \mathrm{tf}(t_4, d_2) \times \mathrm{idf}(t_4) \end{bmatrix}$

Let’s see now a concrete example of this multiplication:

$M_{tf\mbox{-}idf} = M_{train} \times M_{idf} = \\ \begin{bmatrix} 0 & 1 & 1 & 1\\ 0 & 2 & 1 & 0 \end{bmatrix} \times \begin{bmatrix} 0.69314718 & 0 & 0 & 0\\ 0 & -0.40546511 & 0 & 0\\ 0 & 0 & -0.40546511 & 0\\ 0 & 0 & 0 & 0 \end{bmatrix} \\ = \begin{bmatrix} 0 & -0.40546511 & -0.40546511 & 0\\ 0 & -0.81093022 & -0.40546511 & 0 \end{bmatrix}$

And finally, we can apply our L2 normalization process to the$M_{tf\mbox{-}idf}$matrix. Please note that this normalization is“row-wise”because we’re going to handle each row of the matrix as a separated vector to be normalized, and not the matrix as a whole:

$M_ {TF \ MBOX { - } IDF} = \压裂{M_ {TF \ MBOX { - } IDF}} {\ | M_ {TF \ MBOX { - } IDF} \ | _2}$ $= \begin{bmatrix} 0 & -0.70710678 & -0.70710678 & 0\\ 0 & -0.89442719 & -0.4472136 & 0 \end{bmatrix}$

And that is our pretty normalized tf-idf weight of our testing document set, which is actually a collection of unit vectors. If you take the L2-norm of each row of the matrix, you’ll see that they all have a L2-norm of 1.

### Python的实践

从sklearn.feature_extraction.text进口CountVectorizer train_set =（“天空是蓝色的。”，“阳光灿烂”。）TEST_SET =（“在天空中的太阳是光明的。”，“我们可以看到闪耀的太阳，。明亮的太阳“）count_vectorizer = CountVectorizer（）count_vectorizer.fit_transform（train_set）打印 ”词汇“，count_vectorizer.vocabulary＃词汇：{ '蓝'：0， '太阳'：1， '鲜艳'：2 '天空'：3} freq_term_matrix = count_vectorizer.transform（TEST_SET）打印freq_term_matrix.todense（）＃[[0 1 1 1]＃[0 2 1 0]]

from sklearn.feature_extraction.text import TfidfTransformer tfidf = TfidfTransformer(norm="l2") tfidf.fit(freq_term_matrix) print "IDF:", tfidf.idf_ # IDF: [ 0.69314718 -0.40546511 -0.40546511 0. ]

tf_idf_matrix = tfidf.transform(freq_term_matrix) print tf_idf_matrix.todense() # [[ 0. -0.70710678 -0.70710678 0. ] # [ 0. -0.89442719 -0.4472136 0. ]]

And that is it, thetf_idf_matrix其实我们以前$M_{tf\mbox{-}idf}$matrix. You can accomplish the same effect by using the矢量器Scikit的类。学习是一个vectorizer that automatically combines theCountVectorizerTfidfTransformerto you. See这个例子要知道如何使用它的文本分类过程。

I really hope you liked the post, I tried to make it simple as possible even for people without the required mathematical background of linear algebra, etc. In the next Machine Learning post I’m expecting to show how you can use the tf-idf to calculate the cosine similarity.

If you liked it, feel free to comment and make suggestions, corrections, etc.

Cite this article as: Christian S. Perone, "Machine Learning :: Text feature extraction (tf-idf) – Part II," in亚洲金博宝未知领域，03/10/2011，//www.cpetem.com/2011/10/machine-learning-text-feature-extraction-tf-idf-part-ii/

### 参考

Wikipedia :: tf-idf

Sklearn text feature extraction code

### 更新

13 Mar 2015格式化，固定图像的问题。
03 Oct 2011添加了有关使用Python示例环境信息

## 103个想法“机器学习::文本特征提取（TF-IDF） - 第二部分”

1. Severtcev 说：

Wow!
Perfect intro in tf-idf, thank you very much! Very interesting, I’ve wanted to study this field for a long time and you posts it is a real gift. It would be very interesting to read more about use-cases of the technique. And may be you’ll be interested, please, to shed some light on other methods of text corpus representation, if they exists?
（对不起，糟糕的英语，我正在努力对其进行改进，但仍然有很多工作要做的）

2. Excellent work Christian! I am looking forward to reading your next posts on document classification, clustering and topics extraction with Naive Bayes, Stochastic Gradient Descent, Minibatch-k-Means and Non Negative Matrix factorization

而且，scikit学习的文档上的文本特征提取部分（我是罪魁祸首？）真的很差。如果你想给一个手并改善目前的状况，不要犹豫，加入邮件列表。

1. 十分感谢奥利弗。我真的想帮助sklearn，我只是得到一些更多的时间来做到这一点，你们都做了伟大的工作，我真的在lib中已经实现的算法量折服，保持良好的工作！

3. I like this tutorial better for the level of new concepts i am learning here.
That said, which version of scikits-learn are you using?.
最新通过的easy_install安装似乎有不同的模块层次结构（即没有找到sklearn feature_extraction）。如果你能提到你使用的版本，我只是尝试用这些例子。

1. Hello Anand, I’m glad you liked it. I’ve added the information about the environment used just before the section “Python practice”, I’m using the scikits.learn 0.9 (released a few weeks ago).

4. siamii 说：

哪里是第3部分？我必须提交在4天内向量空间模型的分配。把它在周末的希望吗？

1. I’ve no date to publish it since I haven’t got any time to write it =(

5. 说：

再次感谢这个完整和明确的教程和我在等待即将到来的部分。

6. 吴季刚 说：

由于基督徒！与s亚洲金博宝klearn向量空间很不错的工作。我只有一个问题，假设我已经计算了“tf_idf_matrix”，我想计算成对余弦相似性（每行之间）。我是有问题的稀疏矩阵格式，你可以请给出这样的例子？也是我的基质是相当大的，由60K说25K。非常感谢！

7. Khalid 说：

Great post… I understand what tf-idf and how to implement it with a concrete example. But I caught 2 things that I’m not sure about:
1- You called the 2 dimensional matrix M_train, but it has the tf values of the D3 and D4 documents, so you should’ve called that matrix M_test instead of M_train. Because D3 and D4 are our test documents.
2 - 当你计算IDF值的T2（这是“太阳”），它应该是日志（2/4）。因为文件的数目是2 D3有词“太阳” 1次，D4有它的2倍。这使得3，但是我们也加1到值摆脱0分的问题。这使得4 ...我说得对不对还是我失去了一些东西？
Thank you.

1. Victoria 说：

You are correct: these are excellent blog articles, but the author REALLY has a duty/responsibility to go back and correct errors, like this (and others, e.g. Part 1; …): missing training underscores; setting the stop_words parameter; also on my computer, the vocabulary indexing is different.

As much as we appreciate the effort (kudos to the author!), it is also a significant disservice to those who struggle past those (uncorrected) errors in the original material.

1. Victoria 说：

回复：我“你是正确的注释”（上），我应该补充：

“......还注意到康斯登Passot的评论（下同）关于分母：

“......我们用的是什么确实是在发生的一个术语，无论任何给定的文档中出现的术语次数的文件数量。在这种情况下，然后，在用于T2（“太阳”）的IDF值分母确实2 + 1（2个文件具有“太阳”术语，1以避免潜在的零分割误差）。“

2. Yeshwant 说：

Khalid,
This is a response to a very old question. However, I still want to respond to communicate what I understand from the article.
Your question 2: “When you calculate the idf value for the t2 (which is ‘sun’) it should be log(2/4)”
My understanding: The denominator in log term should be (number of documents in which the term appears + 1) and not frequency of the term. The number of documents the term “Sun” appears is 2 (1 time in D3 and 2 times in D4 — totally it appears 3 times in two documents. 3 is frequency and 2 is number of documents). Hence the denominator is 2 + 1 = 3.

8. arzu 说：

感谢...优良的帖子...

9. Jack 说：

优秀的帖子！
I have some question. From the last tf-idf weight matrix, how can we get the importance of term respectively(e.g. which is the most important term?). How can we use this matrix to classify documents

10. Thanuj 说：

Thank You So Much. You explained it in such a simple way. It was really useful. Once again thanks a lot.

11. Thanuj 说：

我有同样的疑问，杰克（最后的评论）。从上个TF-IDF权重矩阵，我们怎么能拿到各自任期的重要性（例如，这是最重要的用语？）。我们如何利用这个矩阵来区分文档。

12. 丁丁 说：

我有个问题..
After the tf-idf operation, we get a numpy array with values. Suppose we need to get the highest 50 values from the array. How can we do that?

1. ashwin sudhini 说：

F（IDF）的高值，表示特定载体（或文件）具有较高的局部强度和低全球实力，在这种情况下，你可以假设，在它的条款具有很高的重要性本地和不能忽视的。针对funtion（TF），其中只有长期重复大量的时间给予更多重视的那些，其中大部分时间是不正确的建模技术比较。

13. Vikram Bakhtiani 说：

Hey ,
Thanx fr d code..was very helpful indeed !

1.适用于文档聚类，计算反相的术语频率之后，shud我使用任何关联性系数等Jaccards系数，然后应用聚类算法中像k均值或shud我计算反转术语频率后直接适用d k均值到文档向量？

2.您是如何评价倒词频为calcuating文档向量文本聚类？

谢谢a ton fr the forth coming reply!

14. @Khalid: what you’re pointing out in 1- got me confused too for a minute (M_train vs M_test). I think you are mistaken on your second point, though, because what we are using is really the number of documents in which a term occurs, regardless of the number of times the term occurs in any given document. In this case, then, the denominator in the idf value for t2 (“sun”) is indeed 2+1 (2 documents have the term “sun”, +1 to avoid a potential zero division error).

我喜欢阅读本系列的第三批呢！我特别想了解更多有关特征选择。是否有一个惯用的方式来获得最高的分数TF.IDF条款的排序列表？你将如何确定这些方面的整体？你将如何得到这是最负责高或低的余弦相似度（逐行）的条款？

Thank you for the _great_ posts!

1. Bonnie Varghese 说：

Should idf(t2) be log 2/4 ?

15. Matthys Meintjes 说：

Excellent article and a great introduction to td-idf normalization.

你必须解释这些复杂的概亚洲金博宝念非常清晰，结构化的方法。

谢谢！

1. 谢谢for the feedback Matthys, I’m glad you liked the tutorial series.

1. PARAM 说：

very good & infomative tutorial…. please upload more tutorials related to documents clustering process.

16. Laurent 说：

Excellent article ! Thank you Christian. You did a great job.

17. Gavin Igor 说：

Can you provide any reference for doing cosine similarity using tfidf so we have the matrix of tf-idf how can we use that to calculate cosine. Thanks for fantastic article.

18. 薰衣草 说：

谢谢so much for this and for explaining the whole tf-idf thing thoroughly.

1. 感谢您的反馈，我很高兴你喜欢这个系列教程。

19. 请纠正我，如果我拨错
与启动后的公式“我们在第一个教程中计算出的频率：”应该不MTEST Mtrain。也开始“这些IDF权重可以由矢量作为表示后：”应该是不idf_test idf_train。

Btw great series, can you give an simple approach for how to implement classification?

20. 迪夫亚 说：

Excellent it really helped me get through the concept of VSM and tf-idf. Thanks Christian

21. 塞尔吉奥 说：

Very good post. Congrats!!

Showing your results, I have a question:

我读了维基百科：
The tf-idf value increases proportionally to the number of times a word appears in the document, but is offset by the frequency of the word in the corpus, which helps to control for the fact that some words are generally more common than others.

When I read it, I understand that if a word apperars in all documents is less important that a word that only appears in one document:

然而，在结果中，“太阳”或“明亮”是比“天空”最重要的。

I’m not sure of understand it completly.

22. Awesome! Explains TF-IDF very well. Waiting eagerly for your next post.

23. awesome work with a clear cut explanation . Even a layman can easily understand the subject..

1. Great thanks for the feedback Rahul !

24. 苏珊 说：

Terrific! I was familiar with tf-idf before but I found your scikits examples helpful as I’m trying to learn that package.

1. I’m glad you liked Susan, thanks for the feedback !

25. Thank you for writing such a detailed post. I learn allot.

26. Eugene Chinveeraphan 说：

优秀的帖子！一次偶然的机会找上CountVectorizer更多信息，无意中发现了这一点，但我很高兴我通过两个您的文章（第1部分和第2部分）的读取。

1. Great thanks for the feedback Eugene, I’m really glad you liked the tutorial series.

27. 说：

似乎没有fit_transform（）为你描述..
Any idea why ?
>>> ts
（“天空是蓝色的”，“阳光灿烂”）
>>> V7 = CountVectorizer（）
>>> v7.fit_transform(ts)
<2×2 sparse matrix of type '’
用4个存储元件在坐标格式>
>>>打印v7.vocabulary_
{u’is’: 0, u’the’: 1}

1. 说：

其实，还有第一个Python样本中的两个小错误。
1. CountVectorizer should be instantiated like so:
count_vectorizer = CountVectorizer(stop_words='english')
这将确保“是”，“的”等被删除。

2.要打印的词汇，你必须在末尾添加下划线。
打印“词汇：” count_vectorizer.vocabulary_

Excellent tutorial, just small things. hoep it helps others.

1. 德罗戈 说：

谢谢ash. although the article was rather self explanatory, your comment made the entire difference.

28. 约翰·凯尔文 说：

29. I’m using scikit learn v .14. Is there any reason my results for running the exact same code would result in different results?

30. Karthik 说：

感谢您抽出时间来写这篇文章。发现它非常有用。亚洲金博宝

31. 维杰 说：

Its useful…..thank you explaining the TD_IDF very elaborately..

32. Mike 说：

谢谢for the great explanation.

我有一个关于IDF（T＃）的计算问题。
In the first case, you wrote idf(t1) = log(2/1), because we don’t have such term in our collection, thus, we add 1 to the denominator. Now, in case t2, you wrote log(2/3), why the denominator is equal to 3 and not to 4 (=1+2+1)? In case t3, you write: log(2/3), thus the denominator is equal 3 (=1+1+1). I see here kind of inconsistency. Could you, please, explain, how did calculate the denominator value.

谢谢。

1. Hello Mike, thanks for the feedback. You’re right, I just haven’t fixed it yet due to lack of time to review it and recalculate the values.

2. xpsycho 说：

你理解错了，分母你不把这个词的总和每个文档中，你只是总结所有具有词的至少一个aparition的文件。

3. MIK 说：

yes, I had the same question…

33. 胡达 说：

This is good post

34. 胡达 说：

it is good if you can provide way to know how use ft-idf in classification of document. I see that example (python code) but if there is algorithm that is best because no all people can understand this language.

谢谢

35. Ganesh神 说：

Great post, really helped me understand the tf-idf concept!

36. Samuel Kahn 说：

漂亮的文章

37. 尼斯。一种解释有助于正确看待这个事情。是TF-IDF的好办法做聚类（例如，从已知的语料用杰卡德分析或方差相对于平均值设定）？

继续写：）

38. 尼Prem 说：

Hi Christian,

这让我非常兴奋和幸运，读亚洲金博宝这篇文章。你理解的清晰反映了文件的清晰度。这让我重拾我的信心在机器学习领域。

由于一吨为美丽的解释。

Would like to read more from you.

谢谢，

1. 大感谢那种wors尼！我很高兴亚洲金博宝你喜欢本系列教程。

39. esra'a OK 说：

谢谢very very much,very wonderful and useful.

1. 感谢您的反馈Esra'a。

40. 阿恩 说：

Thank you for the good wrap up. You mention a number of papers which compare L1 and L2 norm, I plan to study that a bit more in depth. You still know their names?

41. seher 说：

how can i calculate tf idf for my own text file which is located some where in my pc?

42. Shubham 说：

Brilliant article.

By far the easiest and most sound explanation of tf-tdf I’ve read. I really liked how you explained the mathematics behind it.

43. 我hrab 说：

精湛的文章新手

1. Dayananda 说：

优良的材质。优秀的！！！

44. Derrick 说：

嗨，伟大的职位！我使用的是TfidVectorizer模块scikit学习产生与规范= L2的TF-IDF矩阵。我把它叫做tfidf_matrix语料的fit_transform后，我一直在检查TfidfVectorizer的输出。我总结了行，但他们并不总和为1的代码是VECT = TfidfVectorizer（use_idf =真，sublunar_tf =真，规范=” L2）。tfidf_matrix = vect.fit_transform（数据）。当我运行tfidf_matrix.sum（轴= 1）的载体是大于1也许我看错矩阵或我误解如何正常化的作品。我希望有人能澄清这一点！谢谢

45. 克里斯 说：

Can I ask when you calculated the IDF, for example, log(2/1), did you use log to base 10 (e) or some other value? I’m getting different calculations!

46. 贡萨洛·g ^ 说：

伟大的教程，刚开始在ML一份新工作，这很清楚，因为它应该是解释的事情。亚洲金博宝

47. Harsimranpal 说：

Execellent帖子...。！非常感谢这篇文章。

But I need more information, As you show the practical with python, Can you provide it with JAVA language..

48. Sebastian 说：

I’m a little bit confused why tf-idf gives negative numbers in this case? How do we interpret them? Correct me if I am wrong, but when the vector has a positive value, it means that the magnitude of that component determines how important that word is in that document. If the it is negative, I don’t know how to interpret it. If I were to take the dot product of a vector with all positive components and one with negative components, it would mean that some components may contribute negatively to the dot product even though on of the vectors has very high importance for a particular word.

49. Hi,
非常感谢您对这个主题这个详细的解释，真是太好了。无论如何，你可以给我一个提示，这可能是我的错误，我不断看到的来源：

freq_term_matrix = count_vectorizer.transform（TEST_SET）
AttributeError: ‘matrix’ object has no attribute ‘transform’

Am I using a wrong version of sklearn?

50. 莫希特古普塔 说：

谢谢

51. Alexandro 说：

谢谢克里斯，你是唯一一个谁是明确了对角矩阵在网络上。

52. ishpreet 说：

伟大的教程TF-IDF。优秀作品 。请添加对余弦相似性也:)

53. sherlockatsz 说：

I understood the tf-idf calculation process. But what does that matrix mean and how can we use the tfidf matrix to calculate the similarity confuse me. can you explain that how can we use the tfidf matrix .thanks

54. lightningstrike 说：

THX为你的露骨和详细的解释。

55. 匿名 说：

谢谢，好贴，我想它了

56. 匿名 说：

Thank you so much for such an amazing detailed explanation!

57. Akanksha Pande 说：

best explanation.. Very helpful. Can you please tell me how to plot vectors in text classification in svm.. I am working on tweets classification. I am confused please help me.

58. 科希克 说：

I learned so many things. Thanks Christian. Looking forward for your next tutorial.

59. MHR 说：

Hi, I’m sorry if i have mistaken but i could not understand how is ||Vd4||2 = 1.
D4 =的值（0.0，0.89,0.44,0.0），因此归一化将是= SQRT（正方形（0.89）+平方（0.44））= SQRT（0.193）= 0.44
所以我有没有遗漏了什么？请帮我明白了。

60. 李催情 说：

嗨，这是一个伟大的博客！
If I need to do bi-gram cases, how can I use sklearn to finish it?

61. 阿里 说：

这是非常大的亚洲金博宝。我喜欢你教。亚洲金博宝非常非常好

62. 仅限Ritesh 说：

我没有得到相同的结果，当我执行相同的脚本。
print (“IDF:”, tfidf.idf_) : IDF: [ 2.09861229 1. 1.40546511 1. ]

我的Python版本：3.5
Scikit了解的版本是：o.18.1

what does i need to change? what might be the possible error?

谢谢，

1. It can be many things, since you’re using a different Python interpreter version and also a different Scikit-Learn version, you should expect differences in the results since they may have changed default parameters, algorithms, rounding, etc.

1. Ravithej Chikkala 说：

I am also getting: IDF: [2.09861229 1. 1.40546511 1. ]

63. Victor 说：

完美的介绍！
No hocus pocus. Clear and simple, as technology should be.
亚洲金博宝很有帮助
Thank you very much.
请发帖！

64. 亚太区首席技术官Matt南卡尼 说：

为什么| d |= 2，在IDF方程。它不应该是4，因为| d |代表的审议的文件数量，我们有2从测试，2个来自火车。

65. 黎文禅师 说：

这篇文章很有意思。我喜欢这个岗位？

66. Bren 说：

clear cut and to the point explanations….great

67. Shipika Singh 说：

hey , hii Christian
您的文章是真正帮助我了解从基础TFD-IDF。我在分类的一个项目，其中我使用向量空间模型，这导致在确定类别在我的测试文档应该存在。机器学习的一部分。如果你认为我有关的东西这将是巨大的。我被困在这一点上。
谢谢

68. Eshwar S G 说：

看到这个例子就知道如何使用它的文本分类过程。“这个”链接不起作用了。能否请您提供相关链接，例如。

谢谢

69. amanda 说：

such a great explanation! thankyou!

70. alternative investing 说：

wow, awesome post.Much thanks again. Will read on

Say, you got a nice post.Really thank you! Fantastic.

72. togel online 说：

Wow, great article post.Much thanks again. Awesome.

73. 手机电脑 说：

当然有很大的了解这个问题。我真的很喜欢所有的点，你做。

74. chocopie 说：

1vbXlh你提出了一个非常美妙的细节，欣赏它的职位。亚洲金博宝

75. I know this site provides quality based articles or
评论和其他数据，还有没有其他的网页呈现这类
information in quality?

76. Rousse 说：

在第一个例子。IDF（T1），日志（2/1）由计算器= 0.3010。为什么他们获得0.69 ..请有什么不对？

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