<p dir="auto">This is a proposition to deprecate <code class="notranslate">logistic_regression_path</code> and make it private.</p>
<p dir="auto">Context: <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="340206495" data-permission-text="Title is private" data-url="https://github.com/scikit-learn/scikit-learn/issues/11476" data-hovercard-type="pull_request" data-hovercard-url="/scikit-learn/scikit-learn/pull/11476/hovercard?comment_id=413958478&amp;comment_type=issue_comment" href="https://github.com/scikit-learn/scikit-learn/pull/11476#issuecomment-413958478">#11476 (comment)</a></p>
<p dir="auto">Making it private would make it much easier to refactor the code in <code class="notranslate">LogisticRegression</code> and especially in <code class="notranslate">LogisticRegerssionCV</code>, where a lot of case-by-case logic has to be implemented due to non-homogeneous array shapes.</p>
<p dir="auto">The deprecation should not affect users. Quoting a mail from <a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/amueller/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="https://github.com/amueller">@amueller</a> who has looked for occurences of the use of <code class="notranslate">logistic_regression_path</code> on GitHub repositories:</p>
<blockquote>
<p dir="auto">Can confirm it's not used anywhere. I get 12 hits in the 46081 files using sklearn,<br>
all of which are vendoring scikit-learn as a whole (so it's not actually used).<br>
For comparison, LogisticRegression is included in 3980 files (many duplicates and forks of tensorflow)<br>
and in 1662 projects (identified by project name, which is likely underestimating, since not all projects named "kaggle" correspond to the same project).</p>
</blockquote>
<p dir="auto">Hi,</p>
<p dir="auto">I currently have a distance matrix that contains Euclidean distances. However, according to the documentation of AgglomerativeClustering, I cannot use this pre-computed metric as input if I want to use Ward linkage. Ward is reserverd for Euclidean distances only (which makes sense from a theoretical point of view). However, my precomputed distance matrix is within the Euclidean space and therefore I should be allowed to use Ward.</p>
<p dir="auto">Can I just input my distance matrix and run it with "Euclidean" affinity and "Ward" linkage? Or do I need to transform my Euclidean distance matrix to a MDS matrix and input these with "Euclidean" affinity and "Ward" linkage?</p>
<p dir="auto">Maybe include a statement in the documentation: Note, Ward linkage assumes a Euclidean distance matrix. Make sure your pre-computed distance matrix contains Euclidean distances if you want to use Ward linkage.</p>
<p dir="auto">Instead of throwing an error.</p>