42 votes
Accepted

Adaboost vs Gradient Boosting

Both AdaBoost and Gradient Boosting build weak learners in a sequential fashion. Originally, AdaBoost was designed in such a way that at every step the sample distribution was adapted to put more ...
oW_'s user avatar
  • 6,347
40 votes
Accepted

Sentence similarity prediction

Your problem can be solved with Word2vec as well as Doc2vec. Doc2vec would give better results because it takes sentences into account while training the model. Doc2vec solution You can train your ...
Harman's user avatar
  • 706
38 votes

When to use cosine simlarity over Euclidean similarity

When to use cosine similarity over Euclidean similarity Cosine similarity looks at the angle between two vectors, euclidian similarity at the distance between two points. Let's say you are in an e-...
Martin Thoma's user avatar
  • 18.9k
32 votes

Applications and differences for Jaccard similarity and Cosine Similarity

The answer from saq7 is wrong, as well as not answering the question. ∥A∥ means the $L2$ norm of $A$, i.e. the length of the vector in Euclidean space, not the dimensionality of the vector $A$. In ...
user18596's user avatar
  • 421
20 votes
Accepted

How to measure the similarity between two images?

Check this handout! Well, there a few so... lets go: Given two images $J[x,y]$ and $I[x,y]$ with $(x,y) \in N^{N \times M}$... A - Used in template matching: Template Matching is linear and is not ...
Pedro Henrique Monforte's user avatar
18 votes

Best practical algorithm for sentence similarity

Cosine Similarity for Vector Space could be you answer. Or you could calculate the eigenvector of each sentences. But the Problem is, what is similarity? "This is a tree", "This is not ...
Christian Frei's user avatar
14 votes

When to use cosine simlarity over Euclidean similarity

Ok, so, your intuition here is wrong. Not necessarily about the examples you gave, but the fact that you think Euclidian distance could be useful in 200 dimensional space. 200d space is so, so empty. ...
Sam H.'s user avatar
  • 261
14 votes
Accepted

Why use cosine similarity instead of scaling the vectors when calculating the similarity of vectors?

Let $u, v$ be vectors. The "cosine distance" between them is given by $$d_{\cos}(u, v) = 1 - \frac{u}{\|u\|} \cdot \frac{v}{\|v\|} = 1 - \cos \theta_{u,v},$$ and the proposed "...
Mateen Ulhaq's user avatar
12 votes

Applications and differences for Jaccard similarity and Cosine Similarity

Jaccard similarity is used for two types of binary cases: Symmetric, where 1 and 0 has equal importance (gender, marital status,etc) Asymmetric, where 1 and 0 have different levels of importance (...
Vikram Venkat's user avatar
11 votes
Accepted

Cosine similarity vs The Levenshtein distance

As mentioned in other answers, traditionally cosine is used to measure similarity between vectors whereas Levenshtein is used as a string similarity measure, i.e. measuring the distance between ...
Erwan's user avatar
  • 25.3k
10 votes

Sentence similarity prediction

Word Mover’s Distance (WMD) is an algorithm for finding the distance between sentences. WMD is based on word embeddings (e.g., word2vec) which encode the semantic meaning of words into dense vectors. ...
Brian Spiering's user avatar
9 votes

Best practical algorithm for sentence similarity

One approach you could try is averaging word vectors generated by word embedding algorithms (word2vec, glove, etc). These algorithms create a vector for each word and the cosine similarity among them ...
Dani Mesejo's user avatar
  • 2,226
8 votes

Applications and differences for Jaccard similarity and Cosine Similarity

The answer by saq7 is wrong. Where $\mathbf{a}$ and $\mathbf{b}$ are binary vectors, they can be interpreted as sets of indices with value 1. Let's therefore consider sets $A$ and $B$. Jaccard ...
8 votes
Accepted

Text similarity with sentence embeddings

One approach is using Word Mover’s Distance (WMD). WMD is an algorithm for finding the distance between texts of different lengths, where each word is represented as a word embedding vector. The ...
Brian Spiering's user avatar
7 votes
Accepted

How to measure the similarity between two text documents?

In general,there are two ways for finding document-document similarity TF-IDF approach Make a text corpus containing all words of documents . You have to use tokenisation and stop word removal . ...
Pankaj Kumar's user avatar
7 votes

Similarity of words using BERTMODEL

First of all, I think you are confused with pretrained and finetuned. BERT is pretrained on ...
Astariul's user avatar
  • 1,004
5 votes
Accepted

Which supervised learning algorithms are available for matching?

You can try to frame this problem as a recommender systems situation. Where you have your users (prospective students) and items (alumni) and want to recommend to the users one item. It's not a ...
João Almeida's user avatar
5 votes
Accepted

How to compute the Jaccard Similarity in this example? (Jaccard vs. Cosine)

Cosine similarity is for comparing two real-valued vectors, but Jaccard similarity is for comparing two binary vectors (sets). So you cannot compute the standard Jaccard similarity index between your ...
timleathart's user avatar
  • 3,940
5 votes

Sentence similarity prediction

You can try an easy solution using sklearn and it's going to work fine. Use tfidfvectorizer to get a vector representation of each text Fit the vectorizer with your data, removing stop-words. ...
Federico Caccia's user avatar
5 votes

Calculate similarity on boolean data

You should look at the Jaccard Index, is the de facto similarity between set of items, where the sets are represented using a boolean vector. In this boolean vector each coordinate represents an item, ...
Dani Mesejo's user avatar
  • 2,226
5 votes

When to use cosine simlarity over Euclidean similarity

The intuition built by the top response is spot-on for tf-idf vectors, and carries over to any vector that naturally wants to be normalized. However, in such circumstances, cosine similarity is ...
Sean Cantrell's user avatar
5 votes

Similarity score: Can Sklearn SVR predict values greater than 1 and less than 0?

I am using svm.SVR() from scikit-learn to apply Logistic Regression on my training data to solve similarity problem. Wait a second, if you're using support-vector regression, then you're not using ...
zachdj's user avatar
  • 2,684
5 votes

Why use cosine similarity instead of scaling the vectors when calculating the similarity of vectors?

In your example, User 1 and User 2 bought the same ingredients, but User 2 bought 100x more ingredients than User 1. If you normalize and use Euclidean distance, then the distance is 0 (by the ...
Eduard's user avatar
  • 669
4 votes

How can I group texts with similar content together?

Well, after further googling I found the solution: MinHash or SimHash will do the job and I also found a tool implementing MinHash written in Scala on GitHub right at this link
Max's user avatar
  • 191
4 votes

Is there a way to measure correlation between two similar datasets?

I would take a look at Canonical correlation Analysis.
Robin's user avatar
  • 1,337
4 votes
Accepted

Is there a way to measure correlation between two similar datasets?

I see a lot of people post this similar question on StackExchange, and the truth is that there is no methodology to compare if data set A looks like set B. You can compare summary statistics, such as ...
Jon's user avatar
  • 481
4 votes
Accepted

How to find similar time series?

Since the time-series are annual, the data points you have for each time-series are limited and also quite distant (the values are 1 year apart). So I wouldn't use Dynamic Time Wrapping on your data. ...
missrg's user avatar
  • 578
4 votes

Can cosine similarity be applied to multidimensional matrices?

Don't hack. Do the math instead. You could reshape your matrix into a vector, then use cosine. But whether that is sensible to do: ask yourself. You could also ignore the matrix and always return 0....
Has QUIT--Anony-Mousse's user avatar
4 votes

Distance between users

No need for algorithms, or recommendation systems. You have: For each user a have a bunch of features. As long as they're numeric, or can be made numeric (e.g. aggregating the values or one-hot-...
grochmal's user avatar
  • 482
4 votes

Cosine similarity vs The Levenshtein distance

I think the answers you got are technically correct, but don't address the big picture. In the data science world, cosine similarity is mainly used for documents which have been encoded by an ...
Wayne's user avatar
  • 316

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