concise distinction between “Decision Forest" and “Random Forest"

Question

Looking for the best (clearest, shortest, brightest) concise distinction between the ML terms “Decision Forest" and “Random Forest"?

Note the similar and also unanswered question: Multiclass Decision Forest vs Random Forest

Answer 1

Random forests or random decision forests is an extension of the decision forests (ensemble of decision trees) combining bagging and random selection of features to construct a collection of decision trees with controlled variance.

Answer 2

Random forest is an extension of random decision forest that includes bagging. For details check the original paper by Breiman or more lightweight description on Wikipedia. Majority of well-known machine learning libraries, like Python's scikit learn, implements Random Forest.