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Is the computing time of a random forest too long?
The computing time is too long. (It has taken 3 hours so far and it hasn’t finished yet.) I want to know what elements have a big effect on the computing time of a random forest. Is it having factors with too many levels? Are there any optimized methods to improve the RF computing time?
How does random forest work in R-Cross?
About factors; RF (and all tree methods) try to find an optimal subset of levels thus scanning 2 (# of levels-1) possibilities; to this end it is rather naive this factor can give you so much information — not to mention that randomForest won’t eat factors with more than 32 levels.
How are random forests built in machine learning?
Random forests are built using the same fundamental principles as decision trees (Chapter 9) and bagging (Chapter 10 ). Bagging trees introduces a random component into the tree building process by building many trees on bootstrapped copies of the training data.
Why are random forests good for regression trees?
Unfortunately, bagging regression trees typically suffers from tree correlation, which reduces the overall performance of the model. Random forests are a modification of bagging that builds a large collection of de-correlated trees and have become a very popular “out-of-the-box” learning algorithm that enjoys good predictive performance.
Which is faster random forest or caret randomForest?
Finally, the time of randomForest is proportional to nTree. Set nTree=100, and your program will run 5 time faster. I would also just add, that it the main issue is speed, there are several other random forest implementations in caret, and many of them are much faster than the original randomForest which is notoriously slow.
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?
Is the random forest having factors with too many levels?
I am using the party package in R with 10,000 rows and 34 features, and some factor features have more than 300 levels. The computing time is too long. (It has taken 3 hours so far and it hasn’t finished yet.) I want to know what elements have a big effect on the computing time of a random forest. Is it having factors with too many levels?