Question

which hyper parameter when increased may cause random forest to over fit the data

Answer 1

Random Forest is a flexible, easy to use machine learning algorithm that produces, even without hyper-parameter tuning, a great result most of the time. It is also one of the most used algorithms, because it’s simplicity and the fact that it can be used for both classification and regression tasks. In this post, you are going to learn, how the random forest algorithm works and several other important things about it.

Answer 2

The hyper parameter when increased may cause random forest to over fit the data is the Depth of a tree.

Over fitting occurs only when the depth of the tree is increased.

In a random forest the rate of learning is generally not an hyper parameter.

Under fitting can also be caused due to increase in the number of trees.