<p>Yes, the standard way of computing a split for classification trees is decrease in Gini index. An alternative is using Entropy based methods, but results are similar and the formula has logarithms in it, so it is usually slower.</p>

<p>The split using decrease in Accuracy is usually not implemented in packages (it is not in R's randomForest and ranger, nor in Sklearn on python) as id does not respect some basic properties as a loss function and gives straight up bad results. <p>
You can find some details here <a href="https://arxiv.org/pdf/1407.7502.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1407.7502.pdf</a> if you want, around page 42-45</p>
How can I change the split criterion for random forest in R?
<p>I am running a random forest in R with the package randomForest. </p>

<p>I have two questions:</p>

<ol>
<li><p>Is it correct that when using this package the <em>default</em> criterion is Mean Decrease in Gini? </p></li>
<li><p>I plot the variable importance with <code>varImpPlot</code> and obtain two measures of importance: Mean Decrease Accuracy and Mean Decrease Gini; how can I use the former for actually splitting the nodes? </p></li>
</ol>