机器学习::文本特征提取(TF-IDF) - 第二部分

阅读本教程的第一部分:Text feature extraction (tf-idf) – Part I

这个职位是一个Continuationof the first part where we started to learn the theory and practice about text feature extraction and vector space model representation. I really recommend youŤo read the first part后一系列以遵循这个第二。

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


在第一篇文章中,我们学会了如何使用长期频Ťo represent textual information in the vector space. However, the main problem with the term-frequency approach is that it scales up frequent terms and scales down rare terms which are empirically more informative than the high frequency terms. The basic intuition is that a term that occurs frequently in many documents is not a good discriminator, and really makes sense (at least in many experimental tests); the important question here is: why would you, in a classification problem for instance, emphasize a term which is almost present in the entire corpus of your documents ?


But let’s go back to our definition of the\ mathrm {TF}(T,d)which is actually the term count of the termŤ在文档中d。The use of this simple term frequency could lead us to problems like滥用关键字,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) system or even create a bias towards long documents, making them look more important than they are just because of the high frequency of the term in the document.

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.


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从本教程的第一部分中有这样的文字表示:

D4:We can see the shining sun, the bright sun.

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

\vec{v_{d_4}} = (0,2,1,0)

To normalize the vector, is the same as calculating the单位向量矢量,而他们使用的是“帽子”符号表示:\hat{v}。The definition of the unit vector\hat{v}of a vector\ VEC {V}是:

\displaystyle \hat{v} = \frac{\vec{v}}{\|\vec{v}\|_p}

\hat{v}是单位矢量,或者归一化矢量,所述\ VEC {V}是个vector going to be normalized and the\ | \ VEC {V} \ | _p是个ñorm (magnitude, length) of the vector\ VEC {V}in theL^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.

The normalization process (Source: http://processing.org/learning/pvector/)
The normalization process (Source: http://processing.org/learning/pvector/)

但这里的重要问题是如何向量的长度来计算,并明白这一点,你必须了解的动机L^pspaces, also calledLebesgue spaces

Lebesgue spaces

How long is this vector ? (Source: Source: http://processing.org/learning/pvector/)
How long is this vector ? (Source: Source: http://processing.org/learning/pvector/)

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:

(Source: http://processing.org/learning/pvector/)
(Source: http://processing.org/learning/pvector/)

\ | \ VEC【U} \ |= \ SQRT【U ^ 2_1 + U ^ 2_2 + U ^ 2_3 + \ ldots + U ^ 2_n}

But this isn’t the only way to define length, and that’s why you see (sometimes) a numberpŤogether with the norm notation, like in\ | \ VEC【U} \ |_p。That’s because it could be generalized as:

\的DisplayStyle \ | \ VEC【U} \ | _p =(\左| U_1 \右| ^ P + \左| U_2 \右| ^ P + \左| U_3 \右| ^ P + \ ldots + \左|u_n \右| ^ p)^ \压裂{1} {p}


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

So when you read about aL2-norm,you’re reading about the欧几里得范,a norm withp = 2时用于测量的矢量的长度的最常用标准,通常称为“大小”;其实,当你有一个不合格的长度测量(不pñumber), you have theL2-norm(欧几里得范数)。

当你阅读一L1范你正在阅读与规范P = 1,defined as:

\displaystyle \|\vec{u}\|_1 = ( \left|u_1\right| + \left|u_2\right| + \left|u_3\right| + \ldots + \left|u_n\right|)


出租车几何与欧几里得距离:在出租车几何所有三个描绘线具有对于相同的路径具有相同的长度(12)。在欧几里德几何,绿色的线有长度,6 \倍\ SQRT {2} \约8.48,和是个unique shortest path.

请注意,您也可以使用任何规范正常化的载体,但我们将使用最常用的规范,L2范数,这也是在0.9版本的默认scikits.learn。You can also find papers comparing the performance of the two approaches among other methods to normalize the document vector, actually you can use any other method, but you have to be concise, once you’ve used a norm, you have to use it for the whole process directly involving the norm (a unit vector that used a L1-norm isn’t going to have the length 1 if you’re going to take its L2-norm later).

Back to vector normalization

现在you know what the vector normalization process is, we can try a concrete example, the process of using the L2-norm (we’ll use the right terms now) to normalize our vector\vec{v_{d_4}} = (0,2,1,0)in order to get its unit vector\hat{v_{d_4}}。To do that, we’ll simple plug it into the definition of the unit vector to evaluate it:

\hat{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)

这就是它!我们的法矢\hat{v_{d_4}}现在有一个L2范\|\hat{v_{d_4}}\|_2 = 1.0

注意,在这里我们归一化频率Cy 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

Now you have understood how the vector normalization works in theory and practice, let’s continue our tutorial. Suppose you have the following documents in your collection (taken from the first part of tutorial):

Train Document Set: d1: The sky is blue. d2: The sun is bright. Test Document Set: d3: The sun in the sky is bright. d4: We can see the shining sun, the bright sun.

Your document space can be defined then asd = \ {D_1,D_2,\ ldots,D_N \}哪里ñ是个文件数in your corpus, and in our case asD_{train} = \{d_1, d_2\}D_{test} = \{d_3, d_4\}。我们的文档空间的基数被定义\left|{D_{train}}\right| = 2\left|{D_{test}}\right| = 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|}}

哪里\left|\{d : t \in d\}\right|是个文件数其中术语Ť出现,术语频率函数满足当\ mathrm {TF}(T,d)\neq 0,我们只加1代入公式,以避免零分。

The formula for the tf-idf is then:

\mathrm{tf\mbox{-}idf}(t) = \mathrm{tf}(t, d) \times \mathrm{idf}(t)

和该公式具有重要的后果:当你有给定文档中高词频(TF)达到TF-IDF计算的高权重(本地参数)和整个集合中的术语的低文档频率(global parameter).

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}


\ 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)= \日志{\压裂{\左| d \右|} {1+ \左| \ {d:T_2 \在d \} \右|}} = \日志{\压裂{2} {3}} = -0.40546511

\mathrm{idf}(t_3) = \log{\frac{\left|D\right|}{1+\left|\{d : t_3 \in d\}\right|}} = \log{\frac{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)

现在we have our matrix with the term frequency (M_ {}列车) and the vector representing the idf for each feature of our matrix (\ {VEC {idf_列车}}),我们可以计算出我们的TF-IDF权重。我们要做的是矩阵中的每一列的简单乘法M_ {}列车with the respective\ {VEC {idf_列车}}向量维度。要做到这一点,我们可以创建一个正方形diagonal matrixCalledM_ {} IDFwith both the vertical and horizontal dimensions equal to the vector\ {VEC {idf_列车}}dimension:

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}

和ñmultiply it to the term frequency matrix, so the final result can be defined then as:

M_ {TF \ MBOX { - }} IDF= M_{train} \times M_{idf}

请注意,矩阵乘法是不可交换的,结果A \times Bwill be different than the result of the乙\一个时代,这就是为什么M_ {} IDFis 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 theM_ {TF \ MBOX { - }} IDFmatrix. Please note that this normalization is“逐行”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 practice

环境中使用Python v.2.7.2Numpy 1.6.1Scipy v.0.9.0Sklearn(Scikits.learn)v.0.9

Now the section you were waiting for ! In this section I’ll use Python to show each step of the tf-idf calculation using theScikit.learnfeature extraction module.

第一步是创建我们的培训和包括ñg document set and computing the term frequency matrix:

from sklearn.feature_extraction.text import CountVectorizer train_set = ("The sky is blue.", "The sun is bright.") test_set = ("The sun in the sky is bright.", "We can see the shining sun, the bright sun.") count_vectorizer = CountVectorizer() count_vectorizer.fit_transform(train_set) print "Vocabulary:", count_vectorizer.vocabulary # Vocabulary: {'blue': 0, 'sun': 1, 'bright': 2, 'sky': 3} freq_term_matrix = count_vectorizer.transform(test_set) print 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. ]

Note that I’ve specified the norm as L2, this is optional (actually the default is L2-norm), but I’ve added the parameter to make it explicit to you that it it’s going to use the L2-norm. Also note that you can see the calculated idf weight by accessing the internal attribute calledidf_。现在fit()我Ťhod has calculated the idf for the matrix, let’s transform thefreq_term_matrix到TF-IDF权重矩阵:

Ťf_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, theŤf_idf_matrixis actually our previousM_ {TF \ MBOX { - }} IDFmatrix. You can accomplish the same effect by using the矢量器类Scikit.learn的这是一个矢量器自动结合CountVectorizerTfidfTransformerŤo you. See这个例子Ťo know how to use it for the text classification process.

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.

引用本文为:基督教S. Perone,“机器学习::文本特征提取(TF-IDF) - 第二部分”,在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/


Understanding Inverse Document Frequency: on theoretical arguments for IDF

维基百科:: TF-IDF




13 Mar 2015-Formating, fixed images issues.
2011 10月3日-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. 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?

  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


    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?.
    The latest as installed by easy_install seems to have a different module hierarchy (i.e doesn’t find feature_extraction in sklearn). If you could mention the version you used, i will just try out with those examples.

    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. Where’s part 3? I’ve got to submit an assignment on Vector Space Modelling in 4 days. Any hope of putting it up over the weekend?

  5. 谢谢again for this complete and explicit tutorial and I am waiting for the coming section.

  6. 谢谢克里斯Ťian! a very nice work on vector space with sklearn. I just have one question, suppose I have computed the ‘tf_idf_matrix’, and I would like to compute the pair-wise cosine similarity (between each rows). I was having problem with the sparse matrix format, can you please give an example on that? Also my matrix is pretty big, say 25k by 60k. Thanks a lot!

  7. 伟大的职位......我明白了什么TF-IDF以及如何与一个具体的例子实施。但我发现2周的事情,我不知道:
    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- When you calculate the idf value for the t2 (which is ‘sun’) it should be log(2/4). Because number of the documents is 2. D3 has the word ‘sun’ 1 time, D4 has it 2 times. Which makes it 3 but we also add 1 to that value to get rid of divided by 0 problem. And this makes it 4… Am I right or am I missing something?

    1. 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. 回复:我“你是正确的注释”(上),我应该补充:


        “......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. 哈立德,
      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. excellent post!
    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

  9. 非常感谢。你在这样一个简单的方法来解释它。这是非常有用的。再次感谢了很多。

  10. 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。

  11. 我有个问题..

    1. 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。

  12. 嘿,
    Thanx fr d code..was very helpful indeed !

    1.For document clustering,after calculating inverted term frequency, shud i use any associativity coefficient like Jaccards coefficient and then apply the clustering algo like k-means or shud i apply d k-means directly to the document vectors after calculating inverted term frequency ?


    谢谢a ton fr the forth coming reply!

  13. @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).



  14. 优秀文章和一个伟大的介绍TD-IDF正常化。



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

  15. 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.

  16. 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?

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

  18. Very good post. Congrats!!

    Showing your results, I have a question:


    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:



  19. Hello,

    The explanation is awesome. I haven’t seen a better one yet. I have trouble reproducing the results. It might be because of some update of sklearn.
    Would it be possible for you to update the code?

    It seem that the formula for computing the tf-idf vector has changed a little bit. Is a typo or another formula. Below is the link to the source code.


    Many thanks

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

  21. Excellent post! Stumbled on this by chance looking for more information on CountVectorizer, but I’m glad I read through both of your posts (part 1 and part 2).


  22. 似乎没有fit_transform()为你描述..
    >>> TS
    (‘The sky is blue’, ‘The sun is bright’)
    >>> V7 = CountVectorizer()
    >>> v7.fit_transform(ts)
    <2×2 sparse matrix of type '’
    with 4 stored elements in COOrdinate format>

    1. Actually, there are two small errors in the first Python sample.
      1. CountVectorizer should be instantiated like so:
      count_vectorizer = CountVectorizer(STOP_WORDS = '英语')
      This will make sure the ‘is’, ‘the’ etc are removed.

      2. To print the vocabulary, you have to add an underscore at the end.
      打印“词汇:” count_vectorizer.vocabulary_

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

      1. 由于灰。虽然文章是相当自我解释的,您的评论使整个差异。

  23. 感谢伟大的解释。

    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.


    1. 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。

  24. 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.


  25. 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)?


  26. 嗨基督徒,

    It makes me very excited and lucky to have read this article. The clarity of your understanding reflects in the clarity of the document. It makes me regain my confidence in the field of machine learning.

    多谢Ťhe beautiful explanation.

    Would like to read more from you.


  27. 谢谢你的良好的收官之作。你提到一些这比较L1和L2规范的论文,我计划研究,多一点深入。你还知道他们的名字?

  28. 我如何能计算TF IDF为自己的文本文件,它位于一些地方在我的电脑?

  29. 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.

  30. Hi, great post! I’m using the TfidVectorizer module in scikit learn to produce the tf-idf matrix with norm=l2. I’ve been examining the output of the TfidfVectorizer after fit_transform of the corpora which I called tfidf_matrix. I’ve summed the rows but they do not sum to 1. The code is vect = TfidfVectorizer(use_idf=True, sublunar_tf=True, norm=”l2). tfidf_matrix = vect.fit_transform(data). When I run tfidf_matrix.sum(axis=1) the vectors are larger than 1. Perhaps I’m looking at the wrong matrix or I misunderstand how normalisation works. I hope someone can clarify this point! Thanks

  31. 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!

  32. 伟大的教程,刚开始一份新工作在毫升和this explains things very clearly as it should be.

  33. Execellent post….!!! Thanks alot for this article.

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

  34. 我有点困惑,为什么TF-IDF在这种情况下,给出了负数?我们如何解读?纠正我,如果我错了,但是当载体为正值,这意味着该组件的大小确定字是该文件中有多么重要。如果是负数,我不知道如何解释它。如果我是采取向量的点积与所有积极的部件和一个负组件,这将意味着,一些部件可能负点积贡献,即使在载体有一个特定的词非常高的重视。亚洲金博宝

  35. Hi,

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

    Am I using a wrong version of sklearn?

  36. 真棒简单而有效的explaination.Please发布更多的话题与这样真棒explainations.Looking着为即将到来的文章。

  37. Thank you Chris, you are the only one on the web who was clear about the diagonal matrix.

  38. 我明白了TF-IDF计算处理。不过这是什么矩阵均值,以及我们如何使用TFIDF矩阵计算相似度让我困惑。你能解释一下,我们如何利用TFIDF矩阵.thanks

  39. 最好的解释..非常有帮助。亚洲金博宝你能告诉我如何绘制矢量文本分类的SVM ..我在微博分类工作。我很困惑,请帮助我。

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

  41. 您好,我很抱歉,如果我有错,但我不明白是怎么|| VD4 || 2 = 1。
    Ťhe value of d4 = (0.0 ,0.89,0.44,0.0) so the normalization will be = sqrt( square(.89)+square(.44))=sqrt(.193) = .44

  42. 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?

  43. 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. ]

    Scikit Learn version is: o.18.1

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


    1. 它可以很多东西,因为你正在使用一个不同ent 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.

  44. Perfect introduction!
    No hocus pocus. Clear and simple, as technology should be.
    Very helpful
    Thank you very much.
    Keep posting!

  45. 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。

  46. hey , hii Christian

  47. See this example to know how to use it for the text classification process. “This” link does not work any more. Can you please provide a relevant link for the example.


  48. 当然有很大的了解这个问题。我真的很喜欢所有的点,你做。

  49. 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

  50. I know this site provides quality based articles or
    information in quality?

  51. 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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