<h4 dir="auto">Description</h4>

<p dir="auto">I thinks default value of n_clusters is not always 2, as passing the same data points will be clustered as two different clusters which is not possible practically:</p>
<h4 dir="auto">Steps/Code to Reproduce</h4>
<p dir="auto">Example:</p>
<blockquote>
<blockquote>
<blockquote>
<p dir="auto">X = np.array([[1,2],[1,2]])<br>
clustering = AgglomerativeClustering(linkage='single').fit(X)<br>
clustering.labels_<br>
array([1, 0])<br>
clustering = AgglomerativeClustering(linkage='ward').fit(X)<br>
clustering.labels_<br>
array([1, 0])<br>
clustering = AgglomerativeClustering(linkage='average').fit(X)<br>
clustering.labels_<br>
array([1, 0])<br>
clustering = AgglomerativeClustering(linkage='complete').fit(X)<br>
clustering.labels_<br>
array([1, 0])</p>
</blockquote>
</blockquote>
</blockquote>
<h4 dir="auto">Expected Results</h4>
<p dir="auto">array([0,0])</p>
<h4 dir="auto">Actual Results</h4>
<p dir="auto">array([1, 0])</p>
<h4 dir="auto">Versions</h4>
<p dir="auto">For scikit-learn &gt;= 0.20:</p>
<h3 dir="auto">Describe the issue linked to the documentation</h3>
<p dir="auto">There are two <code class="notranslate">and</code> in the description of <code class="notranslate">sklearn.metrics.log_loss</code> function, <a href="https://github.com/scikit-learn/scikit-learn/blob/2beed55847ee70d363bdbfe14ee4401438fba057/sklearn/metrics/_classification.py#L2169-L2170">lines</a></p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="    For a single sample with true label :math:`y \in \{0,1\}` and
    and a probability estimate :math:`p = \operatorname{Pr}(y = 1)`, the log"><pre class="notranslate"><code class="notranslate">    For a single sample with true label :math:`y \in \{0,1\}` and
    and a probability estimate :math:`p = \operatorname{Pr}(y = 1)`, the log
</code></pre></div>
<h3 dir="auto">Suggest a potential alternative/fix</h3>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="    For a single sample with true label :math:`y \in \{0,1\}` and
    a probability estimate :math:`p = \operatorname{Pr}(y = 1)`, the log"><pre class="notranslate"><code class="notranslate">    For a single sample with true label :math:`y \in \{0,1\}` and
    a probability estimate :math:`p = \operatorname{Pr}(y = 1)`, the log
</code></pre></div>