Question

Which of the given hyper parameter(s), when increased may cause random forest to over fit the data? A) Number of Trees B)Depth of Tree C) Learning Rate

Answer 1

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.

Answer 2

Increase in Depth of tree may cause random forest to over fit the data.

Option: B

Explanation:

• Decision trees has the problem of over fitting, especially the tree is very deep.  

• The reason is due to the specific amount leading to smaller sample of events which would meet up previous assumptions. These samples would lead to various unsound conclusions.  

• One such example is Boston Celtics in tonight’s basketball game. One way to solve this issue is to set maximum depth. This will limit the risk of over fitting problem.