Contents
How to tune the number of trees in the random forest?
To tune number of trees in the Random Forest, train the model with large number of trees (for example 1000 trees) and select from it optimal subset of trees. There is no need to train new Random Forest with different tree numbers each time. The number of trees needed in the Random Forest depends on the number of rows in the data set.
Which is better random forest or gradient boosted decision trees?
Random forest regression takes mean value of the results from decision trees. Random forests reduce the risk of overfitting and accuracy is much higher than a single decision tree. Furthermore, decision trees in a random forest run in parallel so that the time does not become a bottleneck.
What makes a random forest a good forest?
The success of a random forest highly depends on using uncorrelated decision trees. If we use same or very similar trees, overall result will not be much different than the result of a single decision tree.
How are N estimators used in a random forest?
There is an additional parameter introduced with random forests: n_estimators: Represents the number of trees in a forest. To a certain degree, as the number of trees in a forest increase, the result gets better. However, after some point, adding additional trees do not improve the model.
How is the prediction of the random forest?
The prediction of the Random Forest is the average from all trees in the subset (I’m doing manually what is done internally in predict_proba in the Random Forest). As ealier, the final response is the average over all 5 models (from internal CV). Notice, that each model from internal CV can have (and have) different number of trees.
Do you need to prune a decision tree in random forest?
Regarding the tree depth, standard random forest algorithm grow the full decision tree without pruning. A single decision tree do need pruning in order to overcome over-fitting issue. However, in random forest, this issue is eliminated by random selecting the variables and the OOB action.
When to lower sample size in random forest?
Lowering bootstrap sample size both gives lower run time and lower tree correlation, thus often a better model performance at comparable run-time. A not so mentioned trick: When RF model explained variance is lower than 40% (seemingly noisy data), one can lower samplesize to ~10-50% and increase trees to e.g. 5000 (usually unnecessary many).