You conduct a Grid Search to find optimal hyperparameters for a Random Forest Classifier. Based on the results shown below, what can you conclude? ALL results are shown $($nothing has been omitted from the rows or columns$).$

$\backslash$table$[[,$param$\_$n$\_$estimators,param$\_$max$\_$leaf$\_$nodes,param$\_$min$\_$samples$\_$split,mean$\_$test$\_$score,rank$\_$test$\_$score$],[0,100,15,10,0\mathrm{.}792473,10],[1,500,15,10,0\mathrm{.}792258,16],[2,100,15,20,0\mathrm{.}798925,7],[3,500,15,20,0\mathrm{.}811613,1],[4,100,15,30,0\mathrm{.}792473,10],[5,500,15,30,0\mathrm{.}805161,4],[6,100,20,10,0\mathrm{.}792473,10],[7,500,20,10,0\mathrm{.}792258,16],[8,100,20,20,0\mathrm{.}798925,7],[9,500,20,20,0\mathrm{.}811613,1],[10,100,20,30,0\mathrm{.}792473,10],[11,500,20,30,0\mathrm{.}805161,4],[12,100,25,10,0\mathrm{.}792473,10],[13,500,25,10,0\mathrm{.}792258,16],[14,100,25,20,0\mathrm{.}798925,7],[15,500,25,20,0\mathrm{.}811613,1],[16,100,25,30,0\mathrm{.}792473,10],[17,500,25,30,0\mathrm{.}805161,4]]$

$$ Based on these results, there were $8$ combinations of hyperparameters tested

The classifier performed better in all cases with fewer trees in the random forest $($holding the other hyperparameters constant$)$

$$ We always get the same results for the same combinations of n$\_$estimators and min$\_$samples$\_$split, regardless of the value of max$\_$leaf$\_$nodes used

$$ Only one optimal combination of hyperparameters was identified in the GridSearch