Notes on understanding why Random Forests makes its decisions.
Understanding Random Forests
A good and visual explanation of how Random Forests works.
Model Feature Importances
Feature importances can be taken from Scikit-learn and Spark MLLib implementations after training.
However, this explains features as a whole based on the training dataset. i.e. We are still lacking visibility on an individual prediction.
Different methods of Explaining
A good overview of ways to explain a random forests model.
Visual explanation for each prediction
This library does the job.