Which one is NOT a characteristics of a decision tree technique?

a$.$

It is an example of the classification algorithms in machine learning.

b$.$

It is an example of the unsupervised learning.

c$.$

Random Forest technique enhances the overfitting problem of decision trees.

d$.$

One of the reasons it is popular is that the output is easily understood by human.