TreeBagger: Random forest or bagged decision trees?

I'm currently building a model using Matlab's TreeBagger function (R2016a). However, I can not find out whether this function implements Breiman's Random forest algorithm or it is just bagging decision trees. Is there someone who can explain to me what TreeBagger does? If someone can explain, I'm also interested in the exact difference between random forests and bagged decision trees!

Thank you for your time!

Answer 1

[michio]

It's Breiman's Random forest algorithm by default.

Ref: Where can I find more information about Breiman's "random forest" method used by TREEBAGGER in Statistics Toolbox 7.1 (R2009a)?

According to Wikpedia, Breiman's random forest algorithm is "Breiman's 'bagging' idea and random selection of features." That's why there's a comment in a doc page about this being Breiman's algorithm except when 'all' is chosen. If 'all' is chosen, the algorithm is just bagged decision trees (bag = bootstrap aggregation).

[CF]

Thank you for your answer!