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@@ -60,7 +60,9 @@ Random forests will select a random number of features on which to apply a weak
\end{center}
\end{figure}
-## Weak Learners comparison
+Changing the randomness parameter had no significant effect on execution time. This can partly be explained by the increased required tree depth to purify the training set.
+
+## Weak Learner comparison
In figure \ref{fig:2pt} it is possible to notice an improvement in recognition accuracy by 1%,
with the two pixels test, achieving better results than the axis-aligned counterpart. The two-pixels