deletions | additions       diff --git a/experiments.tex b/experiments.tex  index 269e879..2a50c20 100644  --- a/experiments.tex  +++ b/experiments.tex 
...
All experiments were run multiple types, with the dataset being shuffled each time. In the case of the Random Forests, the multiple runs of the experiments are necessary because the splitting points for the trees and the dataset splits are chosen randomly across runs.   \subsection{Experiments}  For both tasks, the parameters for the algorithms were selected using cross-validation. In the case of the SVM, the search space was on logarithmic scale from $10^{-2}$ to $10^4$ for the regularization parameter. In the case of the random forest, the number of trees used ranged from 50 150  to 300 250,  in steps of 50, and  the maximum depth number  of features to be sampled at  each tree point  varied from 10 to 100, in steps of 50, and using  the measure of square root,  the quality of the split was either the Gini impurity measure base 2 logarithm, 10\%  or 30\% of  the information gain. total number of features.  Table \ref{table:recog_values} contains the average, maximum and minimum values obtained for the accuracy of the character recognition problem.