How to deal with count data in random forest I am working on a classification model where my target class is a biased class with the class shape as 

    0     1 
 20694   101


Most of my features are the count of number of times a certain event was triggered. While exploring these features I found that my target variable is only associated with certain values of features. For example as below

                 0         1
Feature V1      
0                12014    75
1                6490     16
2                1177     6
3                402      2
4                176      2
5                100    
6                84 
7                61 
8                39 
9                23 
10               26 
11               14 


As we can see that 1 only occurs when V1 has value of 0 to 4.Thus for any unseen data my model would always predict 0 whenever V1 has value greater than 4.

I thought of using bestNormalize package, however the transformations it is suggesting looses correlation when applied to the data.

Any suggestion would be of great help. 

Thanks a lot in advance!!

P.S. Happy to share the data if required.
Some thoughts:


Your data is highly imbalanced. This is a critical issue which should be dealt with. Possible solutions include simple under-/over-sampling to more complicated synthetic approaches like SMOTE.
Decision trees and random forests do not require feature scaling - this means that normalisation is not needed (unless perhaps you plan on using some other modelling technique which uses regularisation).
Just because this data set only shows a particular relationship between values of V1 and target does not mean it is always the case - especially if your model is to be deployed for a period of time. The relationship may change over time so do not rush to artificially curtail your model.