Contents
How to train a random forest in caret?
Using the defaults of the train in caret package, I am trying to train a random forest model for the dataset xtr2 (dim (xtr2): 765 9408). The problem is that it unbelievably takes too long (more than one day for one training) to fit the function.
Why does training take so long in random forest?
The problem is that it unbelievably takes too long (more than one day for one training) to fit the function. As far as I know train in its default uses bootstrap sampling (25 times) and three random selection of mtry, so why it should take so long?
Where can I find the manual for caret?
Overall, the online manual for caret is quite good: http://topepo.github.io/caret/index.html. Good luck! You use train for determining mtry only.
How many parts is too many in random forest?
If you do 10 fold cross-validation (I am not sure it should be done anyways, as validation is ingrained into the random forest), 10 parts is too much, if you are short in time. 5 parts would be enough. Finally, the time of randomForest is proportional to nTree. Set nTree=100, and your program will run 5 time faster.
How to train a random forest in R?
Train a random forest model. The algorithm uses 500 trees and tested three different values of mtry: 2, 6, 10.The final value used for the model was mtry = 2 with an accuracy of 0.78. Let’s try to get a higher score. Step 2) Finding best mtry
How does random forest regression work with estimators?
Average prediction across estimators. Each decision tree regression predicts a number as an output for a given input. Random forest regression takes the average of those predictions as its ‘final’ output. Let’s delve deeper into how random forest regression builds regression trees.
How to make predictions in a random forest?
To make a prediction, we just obtain the predictions of all individuals trees, then predict the class that gets the most votes. This technique is called Random Forest. We will proceed as follow to train the Random Forest: