# How to set class_weight parameter for cost sensitive learning?

I'm dealing with a binary classification problem with a balanced data set, however false positives are much more costly than false negatives. Let's just say that an FP is in general 3 times more costly than an FN and the response variable = 1 means a positive identification. How should I set the class_weight parameter in sklearn's RandomForest to reflect this ? From my understanding, I would say:

class_weight = {0:1.0,1:3.0}


I'm not sure if I understand this parameter correctly or should it be the inverse ?

Thanks.

$$\begin{array} {|r|r|} \hline - &(1) &(0) \\ \hline (1) &- &FP\\ \hline (0) &FN &-\\ \hline \end{array}$$
class_weight = {0:3.0.0,1:1.0}