Understanding random forests with randomForestExplainer

Next, we pass it to the function plot_min_depth_distribution and under default settings obtain obtain a plot of the distribution of minimal depth for top ten variables according to mean minimal depth calculated using top trees (mean_sample "top_trees"). We could also pass our forest directly to the plotting function but if we want to make more than one plot of the minimal depth distribution is more efficient to pass the min_depth_frame to the plotting function so that it will not be calculated again for each plot (this works similarly for other plotting functions of randomForestExplainer). The function plot_min_depth_distribution offers three possibilities when it comes to calculating the mean minimal depth, which differ in he way they treat missing values that appear when a variable is not used for splitting in a tree. Note that the depth of a tree is equal to the length of the longest path from root to leave in this tree. This equals the maximum depth of a variable in this tree plus one, as leaves are by definition not split by any variable.