We have published a technical paper http://arxiv.org/abs/1605.09196 describing the forest floor visualizations.
This figure from paper uses a simulated data set(1) [y = -X1^2 – cos(-X2) + noise], which is modelled with random forest. (2) is the out-of-bag predictions and (3) is any test set prediction. Feature contribution method is used to decompose (2) and (3) into (2a/2b) and (3a/3b). Projections of feature contributions (grey surfaces) split model into non-linaer main effects and interactions, which is a useful approach to investigate a learned random forest model structure. Goodness-of-visualization: It is possible to test how well, a visualization describes the random forest model structure, by testing how well the high dimensional model structure can be reconstructed from low dimensional visualizations.