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

<p dir="auto">The first and second image of <a href="http://scikit-learn.org/dev/modules/mixture.html#estimation-algorithm-variational-inference" rel="nofollow">the doc of Variable Bayesian Gaussian Mixture</a> are the same. This is due to a simple typo in the file name.</p>
<p dir="auto">While fixing this, as per <a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/tguillemot/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="https://github.com/tguillemot">@tguillemot</a> 's advice, it might be beneficial to modify <a href="https://github.com/scikit-learn/scikit-learn/blob/master/examples/mixture/plot_concentration_prior.py#L74">plot_concentration_prior.py</a> too, since the default value of the <code class="notranslate">align</code> parameter of <code class="notranslate">ax.bar()</code> has changed from <code class="notranslate">edge</code> to <code class="notranslate">center</code> in matplotlib 2.0 (<a href="http://matplotlib.org/users/dflt_style_changes.html#bar-and-barh" rel="nofollow">changelog</a>), so the bar charts are off when the script is run with matplotlib 2.</p>
<p dir="auto">I am sending a PR.</p>
<h4 dir="auto">Describe the issue linked to the documentation</h4>
<p dir="auto">Some models have many parameters.<br>
Some of those parameters are very important, some others much less so.<br>
While sklearn provides sound defaults usually, it would be nice<br>
if a potential user sees right away which parameters <em>must</em><br>
be optimized in order to get a significantly better model.</p>
<h4 dir="auto">Suggest a potential alternative/fix</h4>
<p dir="auto">For example, in RandomForestsRegressor (and Classifier), n_trees<br>
is a critical parameter (while I have no idea for the other ones, honestly).</p>
<p dir="auto">In linearSVR, C is a critical parameter (epsilon is not, in my experience).</p>
<p dir="auto">Let's define a way to tag such parameters in the documentation, and let expert users<br>
tag the critical parameters for models they have experience working with.</p>