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Random forest is a machine-learning method based on combining the outputs of many decision trees.
3
votes
0
answers
137
views
Random Forest Models: creating correlated features
I'm trying to understand how correlated (multicollinear) predictors affect predictive power and / or variable importance in tree models, e.g. Random Forest models. Particularly, I'd like to know if ce …
1
vote
1
answer
795
views
randomForest::varImp VS conditional variable importance [closed]
Data:
My training set consists of ~450k obs and 26 variables, out of which 1 is an ordinal factor (order_month, 12 levels) and the rest is numerical. Moreover, some of my predictors are highly corre …
2
votes
Variable importance in party vs randomForest
Traditional Random Forest uses "Gini Gain" splitting criterion in assessing Variable Importance, which is biased towards factor variables with many levels/categories. In contract, cforest function cre …