Mar 13, 2015 · Old thread, but I don't agree with a blanket statement that collinearity is not an issue with random forest models. When the dataset has two (or more) correlated features, then from the point of view of the model, any of these correlated features can be used as the predictor, with no concrete preference of one over the others.

A question that has been bugging me recently is whether it is more correct to refer to the Random Forests Classifier as "Random Forests" or "Random Forest" (e.g. "We implemented a Random Forest classifier" or "We implemented a Random Forests classifier".) Which is more correct, or are both equally correct? Breiman in his classic paper https ...

Oct 21, 2015 · 2) As I alluded to above, R's random forest implementation can only handle 32 factor levels - if you have more than that then you either need to split your factors into smaller subsets, or create a dummy variable for each level.

Jan 1, 2019 · The package addresses cross level interaction by first running random forest as the local classifier at each parent node of the class hierarchy. Next the predict function retrieves the proportion of out of bag votes that each case received in each local classifier.

Aug 15, 2014 · $\begingroup$ Empirically, I have not found it difficult at all to overfit random forest, guided random forest, regularized random forest, or guided regularized random forest. They regularly perform very well in cross validation, …

Jul 21, 2015 · Random Forest: IncNodePurity and Feature Selection for Binary Logistic Regression. 2. The importance() in ...

Jun 6, 2017 · Fit a random forest. Note the % variance explained. Do steps 1-3 multiple times, say 1,000-10,000 times. You now have an empirical distribution of % variance explained through a random forest, under the null hypothesis of no relationship between your independent and dependent variable.

Feb 29, 2016 · In fact, each single random forest's tree is trained on a bootstrapped sample of the training set, so meanwhile it is true that on average every single tree sees about 66% of the training set, but overall the random forest ensemble sees more than that (depending on the number of trees fitted) because the out-of-bag samples of individual trees ...

samplesize.ratio a random number between 10% and 100%, the ratio size of each bootstrap all models were trained like rfo = randomForest(x=X, y=Ytotal, <more args>) the randomForest.performance , its ability to explain the highest fraction of the TEV increases in general when samplesize lowers when the TEV is less than 50% and decrease when TEV ...

However, generally random forests would give good performance with full depth. As random forests training use bootstrap aggregation (or sampling with replacement) along with a random selection of features for a split, the correlation between the trees (or weak learners) would be low.