Are Random Forests Interpretable?

It might seem surprising to learn that Random Forests are able to defy this interpretability-accuracy tradeoff, or at least push it to its limit. After all, there is an inherently random element to a Random Forest's decision-making process, and with so many trees, any inherent meaning may get lost in the woods.

Is random forest non parametric?

Both random forests and SVMs are non-parametric models (i.e., the complexity grows as the number of training samples increases). ... The complexity of a random forest grows with the number of trees in the forest, and the number of training samples we have.

Are random forests ensembles?

Random forest is an ensemble of decision tree algorithms. It is an extension of bootstrap aggregation (bagging) of decision trees and can be used for classification and regression problems.