<p>I think you should read the <a href="https://arxiv.org/pdf/1703.09039.pdf" rel="nofollow noreferrer">DNN</a> article.</p>
<p>Why? Why do you want to use Random Forest before DNN training?</p>
<p>Yes, you can display the feature importance of <code>random-forest</code> using</p>
<pre><code>random_forest = RandomForestClassifier(random_state=42).fit(x_train, y_train)

feature_importances = DataFrame(random_forest.feature_importances_,
                                index = x_train.columns,
                                columns=['importance']).sort_values('importance', 
                                                                 ascending=False)
    
print(feature_importances)
</code></pre>
<p>But this is a <code>feature-extraction</code> method. The DNN is a <code>neural-network</code> method.</p>
<p>DNN is more complex than <code>random-forest</code>, while <code>random-forest</code> handles <code>feature-extraction</code>, DNN handles</p>
<ul>
<li><code>feature-extraction</code>,</li>
<li><code>back-propagation</code>,</li>
<li><code>feed-forward</code> methods.</li>
</ul>
<p>If you feed enough training samples for DNN, you will have higher accuracy.</p>
<ul>
<li>Does the use of tree-based feature importance prevents the use of training algorithms?</li>
</ul>
<p>No, based on the problem, the sufficient feature size and samples vary. Usually, you don't use <code>random-forest</code> to extract 1M images feature importance.</p>
<p>Also, you don't use DNN for small-datasets.</p>
Tree-Based dimensionality reduction in DNN algorithms
<p>My question is straightforward: is it possible to use a tree-based dimensionality reduction such as feature importance embedded in the Random Forest before training the dataset with a DNN algorithm?</p>
<p>In other words, does the use of tree-based feature importance prevents the use of training algorithms different from the tree/Random Forest?</p>