This is due to the fact that the minimum requirement of splitting a node is so high that there are no significant splits observed. As a result, the random forest starts to underfit. You can read more about the concept of overfitting and underfitting here: ... Overfitting in Machine Learning.
How do you stop Underfitting in random forest?
Q31) To reduce under fitting of a Random Forest model, which of the following method can be used?
- Increase minimum sample leaf value.
- increase depth of trees.
- Increase the value of minimum samples to split.
- None of these.
Does overfitting happen in random forest?
Random Forests do not overfit. The testing performance of Random Forests does not decrease (due to overfitting) as the number of trees increases. Hence after certain number of trees the performance tend to stay in a certain value.
