I got this as a result (I can provide more information about the data itself if you would like it). I was curious about how one should interpret this chart that was created in R. I understand it has to do with the importance of variables in the model that was created, but what else can be derived from this chart?
You should check out R's documentation on the function you used to generate this plot, which was probably varImpPlot().
The varImpPlot() function generates a visual representation of importance()'s output.
As per the docs, this is the meaning of importance() output:
The first measure is computed from permuting OOB data: For each tree, the prediction error on the out-of-bag portion of the data is recorded (error rate for classification, MSE for regression). Then the same is done after permuting each predictor variable. The difference between the two are then averaged over all trees, and normalized by the standard deviation of the differences. If the standard deviation of the differences is equal to 0 for a variable, the division is not done (but the average is almost always equal to 0 in that case).
The second measure is the total decrease in node impurities from splitting on the variable, averaged over all trees. For classification, the node impurity is measured by the Gini index. For regression, it is measured by residual sum of squares.
A more detailed explanation on these measures is given here.
