The predictions in this tool are made using random forest regression models. Each machine learning model here captures feature information for a given network size at a given loss %. Hence, each heatmap illustrates a comparison of samples at 7 different loss percentages (%s). In total, there are 35 different regression models. Feature importance is a metric to measure the significance of a feature using random forest regression algorithm.The following are the feature importances (each heatmap cell is an average of feature importances across 100 runs) for every network size (100, 200, 300, 400 and 500). 100 network size contains samples. 200 network size contains samples. 300 network size contains samples. 400 network size contains samples. 500 network size contains samples. The distribution of variance within the importance of each feature (across 100 runs) will be shortly available as figures.