The final optimized classification model resulted in an accuracy of 94% and 92% for the training and test set, respectively (Figures and). In this case 200 trees and 8 minimum samples per leaf was found to be the optimum for the model (Figure ). For the training and test set, 90.8% and 87.7% of the 'inside' and 'maybe' cases were correctly classified, 7.4% and 9.3% of the 'inside' and 'maybe' cases were wrongly classified as a 'maybe' or 'inside' case, respectively, and 1.7% and 3.0% of the 'inside' and 'maybe' cases were wrongly classified as 'outside'. For the 'outside' cases, 0.7% and 1.5% of the cases were wrongly classified as an 'inside' or 'maybe' case and 99.3% and 98.5% of the cases was correctly classified as an 'outside' case for the training and test set, respectively. Overall, considering that the wrongly classified 'inside' and 'maybe' cases as 'maybe' and 'inside', respectively, still are considered part of the RPLC subspace, the performance of the model was very good with only 2.4% of all cases being wrongly classified as 'inside' or 'maybe' while being an 'outside'