Contents
Can you have too many trees in random forest?
So the optimal number of trees in a random forest depends on the number of predictors only in extreme cases. The official page of the algorithm states that random forest does not overfit, and you can use as much trees as you want.
What should I tune for random forest?
The most important hyper-parameters of a Random Forest that can be tuned are:
- The Nº of Decision Trees in the forest (in Scikit-learn this parameter is called n_estimators)
- The criteria with which to split on each node (Gini or Entropy for a classification task, or the MSE or MAE for regression)
Do random forests reduce Overfitting?
The Random Forest algorithm does overfit. The generalization error variance is decreasing to zero in the Random Forest when more trees are added to the algorithm. To avoid overfitting in Random Forest the hyper-parameters of the algorithm should be tuned. For example the number of samples in the leaf.
Why is maximum number of leaf nodes important in random forest?
Maximum number of leaf nodes. In Random Forest this is not so important, but in an individual Decision Tree it can greatly help reduce over-fitting as well and also help increase the explainability of the tree by reducing the possible number of paths to leaf nodes.
Is there a difference between tree depth and number of trees in random forest?
An article from Oshiro et al. (2012) pointed out that, based on their test with 29 data sets, after 128 of trees there is no significant improvement (which is inline with the graph from Soren). Regarding the tree depth, standard random forest algorithm grow the full decision tree without pruning.
Why are decision trees important in random forest?
In Random Forest this is not so important, but in an individual Decision Tree it can greatly help reduce over-fitting as well and also help increase the explainability of the tree by reducing the possible number of paths to leaf nodes. Learn how to use Decision Trees to build explainable ML models here.
What should the error rate be in a random forest?
The first consideration is the number of trees within your random forest. Although not technically a hyperparameter, the number of trees needs to be sufficiently large to stabilize the error rate.