Answers.Body: <p>You can compute <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html" rel="nofollow noreferrer">Silhouette Coefficient</a> for your aim. Its values <a href="https://towardsdatascience.com/silhouette-coefficient-validating-clustering-techniques-e976bb81d10c" rel="nofollow noreferrer">mean</a>:</p>
  <blockquote>
  <p>1: Means clusters are well apart from each other and clearly distinguished.</p>
  <p>0: Means clusters are indifferent, or we can say that the distance between clusters is not significant.</p>
  <p>-1: Means clusters are assigned in the wrong way.</p>
  </blockquote>
  <p>Other measures, such as <a href="https://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-clustering-1.html" rel="nofollow noreferrer">purity and mutual information</a>, are also possible by computing</p>
  <blockquote>
  <p>an external criterion that evaluates how well the clustering matches the gold standard classes</p>
  </blockquote>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-02-09T21:28:53.227Z
Answers.LastActivityDate: 2022-05-13T08:26:20.677Z
Answers.LastEditDate: 2022-05-13T08:26:20.677Z
Answers.UserDisplayName: OmG
Answers.Body: <p>One more popular metric for this is the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.davies_bouldin_score.html#sklearn.metrics.davies_bouldin_score" rel="nofollow noreferrer">Davies Bouldin Score</a>.</p>
  <p>You can also take a look at the clustering metrics in <a href="https://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics" rel="nofollow noreferrer">scikit documentation</a>.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-02-10T23:19:28.023Z
Answers.LastActivityDate: 2022-05-13T08:27:26.900Z
Answers.LastEditDate: 2022-05-13T08:27:26.900Z
Answers.UserDisplayName: Abhishek Verma
Body: <p>I have a model that outputs a latent <strong>N-dimensional embedding</strong> for all data points, trained in a way that clusters data-points from the same class together, while being separated from other clusters belonging to other different classes.</p>
  <p>The N-dimensional embedding is projected down to 2D using UMAP. At each epoch, I wish to test the <strong>clustering capability</strong> of the model on these 2D projections for use as validation accuracy. I have the labels for each class.</p>
  <p>How should I proceed?</p>
  <p><a href="https://i.sstatic.net/DvTCF.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/DvTCF.png" alt="UMAP Projection " /></a></p>
  
Tags: <unsupervised-learning><clustering><metric><umap>
Title: Which metric should I use to assess the quality of the clusters?
UserDisplayName: jaeger6
ThreadText: # Which metric should I use to assess the quality of the clusters?
  
  (Asked by: username_0 on 2021-02-09)
  
  I have a model that outputs a latent **N-dimensional embedding** for all data points, trained in a way that clusters data-points from the same class together, while being separated from other clusters belonging to other different classes.
  
  The N-dimensional embedding is projected down to 2D using UMAP. At each epoch, I wish to test the **clustering capability** of the model on these 2D projections for use as validation accuracy. I have the labels for each class.
  
  How should I proceed?
  
  [![UMAP Projection ](https://i.sstatic.net/DvTCF.png)](https://i.sstatic.net/DvTCF.png)
  
  **Tags:** unsupervised-learning, clustering, metric, umap
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2021-02-09. Score: 2
  
  You can compute [Silhouette Coefficient](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html) for your aim. Its values [mean](https://towardsdatascience.com/silhouette-coefficient-validating-clustering-techniques-e976bb81d10c):
  
  > 1: Means clusters are well apart from each other and clearly distinguished.
  >
  > 0: Means clusters are indifferent, or we can say that the distance between clusters is not significant.
  >
  > -1: Means clusters are assigned in the wrong way.
  
  Other measures, such as [purity and mutual information](https://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-clustering-1.html), are also possible by computing
  
  > an external criterion that evaluates how well the clustering matches the gold standard classes
  
  
  ---
  
  ### Answer by username_2 on 2021-02-10. Score: 1
  
  One more popular metric for this is the [Davies Bouldin Score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.davies_bouldin_score.html#sklearn.metrics.davies_bouldin_score).
  
  You can also take a look at the clustering metrics in [scikit documentation](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics).
  
  
  ---
  
  