A random forest model with 500 trees shows no improvement in OOB error beyond 200 trees. What does this demonstrate about random forests?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because random forests exhibit convergence behavior — OOB error stabilizes as trees increase. Unlike boosting, random forests do not overfit with more trees.
Full explanation below image
Full Explanation
B is correct because random forests exhibit convergence behavior — OOB error stabilizes as trees increase. Unlike boosting, random forests do not overfit with more trees. A is wrong; overfitting from more trees is a boosting concern, not random forests. C is wrong; square root of features applies to VARS_TO_TRY, not tree count. D is wrong; random forests do not require early stopping.