<p dir="auto">This is a sequel to <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="47923503" data-permission-text="Title is private" data-url="https://github.com/scikit-learn/scikit-learn/issues/3830" data-hovercard-type="issue" data-hovercard-url="/scikit-learn/scikit-learn/issues/3830/hovercard" href="https://github.com/scikit-learn/scikit-learn/issues/3830">#3830</a>. Those are improvements identified by <a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/jnothman/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="https://github.com/jnothman">@jnothman</a> to improve the PDF output of running the <code class="notranslate">make latexpdf</code> command in the doc folder.</p>
<ul dir="auto">
<li>there is a couple of useless blank pages before the first chapter</li>
<li>there should be a table-of-contents</li>
<li>there seem to be many chapters that should really be sections of other chapters (e.g. the one on fetching covertype; changelong entries)</li>
<li>what would be thumbnails corresponding to examples on the web site show up as duplicated images in the examples themselves, as well as as in the examples associated with each class/function in the reference</li>
<li>assume within-documentation references should be hyperlinked, but do not seem to be in cases where I've looked</li>
</ul>
<p dir="auto">In this article <a href="https://link.springer.com/article/10.1186/1471-2105-8-25" rel="nofollow">https://link.springer.com/article/10.1186/1471-2105-8-25</a> concerns about using gini for feature importance is being raised.</p>
<blockquote>
<p dir="auto">We found that for the original random forest method the variable importance measures are affected by the number of categories and scale of measurement of the predictor variables, which are no direct indicators of the true importance of the variable.</p>
<p dir="auto">However, in studies where continuous variables, such as the folding energy, are used in combination with categorical information from the neighboring nucleotides, or when categorical predictors, as in amino acid sequence data, vary in their number of categories present in the sample variable selection with random forest variable importance measures is unreliable and may even be misleading.</p>
</blockquote>
<p dir="auto">I'm not entirely sure but as I understand the documentation and reading through the documentation and code feature importance is calculated by using either gini or entropy (both suffering from the same issue I suppose). Is their any way circumvent this problem or actions that is supposed to be taken before feature imortance.</p>
<p dir="auto">In the article they propose a new splitting criteria to combat this problem.</p>