Interview question: What is the difference between Tensorflow’s Random Forest model and Tensorflow’s Gradient Boosted Trees Model?

Tensorflow is the premiere library to create neural networks in the Python programming language. I took my time to begin studying this library because, having previously studied Pytorch, I believed that it would be very difficult to learn.

One thing that I have learned since I decided to study Tensorflow to add this skill to my repertoire is the fact that Tensorflow has a number of algorithms that would not feel out of place in it’s sister library, sklearn, such as: linear regression, logistic regression, random forest, and gradient boosted trees.

The random forest model and gradient boosted trees model are not in the Tensorflow library, but are housed in a separate library, being tensorflow_decision_forests. I have been studying these two models that are in the library, and I had a prominent question, which is:-

What is the difference between Tensorflow’s random forest model and Tensorflow’s gradient boosted trees model?