<p>Both, case 1 and 2 are called ensemble learning. Both are worth it. </p>

<p>For case 1: please note that neural networks with the same architecture, same learning algorithm but different initial weights can have very different performance.</p>

<p>Similar to case 1 (taking the average, not the mode) are Schmidhubers averaging ensembles. I published some results of them with various datasets and network architectures in <a href="https://arxiv.org/abs/1707.09725" rel="nofollow noreferrer">my masters thesis</a> (e.g. Table 5.2, Table 5.8, Table 5.9, Table 5.11, 5.13, ...).</p>

<h2>See also</h2>

<ul>
<li><a href="http://scikit-learn.org/stable/modules/ensemble.html" rel="nofollow noreferrer">Ensemble methods in sklearn</a></li>
<li>My Blog post: <a href="https://martin-thoma.com/ensembles/" rel="nofollow noreferrer">Ensembles</a></li>
<li><a href="https://stats.stackexchange.com/a/187970/25741">Bagging, boosting and stacking in machine learning</a></li>
</ul>
Is taking the mode of a duplicate classifier worth it?
<p>I have a question about a classifier concept.</p>
<h2>case 1</h2>
<p>If I have a classifier and the performance of that classifier up to 90%.
and I create n another classifier with the same algorithm and same dataset and get performance 90% too.</p>
<h2>case2</h2>
<p>same like case 1 but every classifier with a different algorithm.</p>
<h2>Combinding the results</h2>
<p>the result I get from</p>
<pre><code>mode(classifier1,classifier2,classifier3,...,classifiern)
</code></pre>
<p>Is that technique worth it or useless (case1, and case2)?</p>