Body: <p>The sklearn's documentation of the method  <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html" rel="nofollow noreferrer"><code>roc_auc_score</code></a> states that the parameter <code>multi_class</code> can take the value <code>'OvR'</code> (which stands for <strong>One-vs-Rest</strong>) or <code>'OvO'</code> (which stands for <strong>One-vs-One</strong>). These values are only applicable for multi-class classification problems.</p>
  <p>Does anyone know in what particular cases we would use OvR as opposed to OvO? In the general academic literature, is there a preference given to one?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-06T13:47:04.160Z
Comments.Text: The linked scikit-learn page already references three (3) academic publications on this - did you check them?
Comments.UserDisplayName: desertnaut
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-06T22:01:27.090Z
Comments.Text: This time I decided to clarify your post by editing it, but it's not my job to do it: it's yours. Please, next time, provide the necessary details to answer the question and do not assume that we have been reading what you have also read or know what you're talking about. As someone pointed out above, some acronyms may not be well-known, for instance.
Comments.UserDisplayName: nbro
Tags: <machine-learning><metric><scikit-learn><roc-auc><multiclass-classification>
Title: When computing the ROC-AUC score for multi-class classification problems, when should we use One-vs-Rest and One-vs-One?
UserDisplayName: Leockl
ThreadText: # When computing the ROC-AUC score for multi-class classification problems, when should we use One-vs-Rest and One-vs-One?
  
  (Asked by: username_0 on 2021-01-06)
  
  The sklearn's documentation of the method [`roc_auc_score`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html) states that the parameter `multi_class` can take the value `'OvR'` (which stands for **One-vs-Rest**) or `'OvO'` (which stands for **One-vs-One**). These values are only applicable for multi-class classification problems.
  
  Does anyone know in what particular cases we would use OvR as opposed to OvO? In the general academic literature, is there a preference given to one?
  
  **Tags:** machine-learning, metric, scikit-learn, roc-auc, multiclass-classification
  
  > **Comments:**
  > **username_1**: The linked scikit-learn page already references three (3) academic publications on this - did you check them?  
  > **username_2**: This time I decided to clarify your post by editing it, but it's not my job to do it: it's yours. Please, next time, provide the necessary details to answer the question and do not assume that we have been reading what you have also read or know what you're talking about. As someone pointed out above, some acronyms may not be well-known, for instance.  
  
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  ## 0 answers
  
  