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Random Forest is very popular as a variable selection technique. However, it has some drawbacks as well (listed below)

1. If independent variables are of different type (for example: some continuous or some categorical), random forest (randomForest package in R) variable importance measure can be misleading. To overcome this problem, we should use conditional inference forest i.e. cforest (party package)

2. If independent variables are correlated, random forest (randomForest package in R) variable importance measure can be misleading. Even, conditional forest does not remove multicollinearity problem completely.It solves collinearity problem to some extent. While using conditional inference forest i.e. cforest (party package), we can include option

3. If independent variables are all categorical but having different categories, random forest (randomForest package in R) variable importance measure can be misleading. To overcome this problem, we should use conditional inference forest i.e. cforest (party package).

**:**1. If independent variables are of different type (for example: some continuous or some categorical), random forest (randomForest package in R) variable importance measure can be misleading. To overcome this problem, we should use conditional inference forest i.e. cforest (party package)

2. If independent variables are correlated, random forest (randomForest package in R) variable importance measure can be misleading. Even, conditional forest does not remove multicollinearity problem completely.It solves collinearity problem to some extent. While using conditional inference forest i.e. cforest (party package), we can include option

**varimp (obj, conditional=TRUE)**which can add to the understanding of data

3. If independent variables are all categorical but having different categories, random forest (randomForest package in R) variable importance measure can be misleading. To overcome this problem, we should use conditional inference forest i.e. cforest (party package).

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