Choosing weights on random forest for imbalanced data with the aim to minimize false positives I am currently dealing with a binary classification task on imbalanced data with the following distribution:  

y_train: 4981 positive / 863894 negative samples  
y_test:  128  positive / 128309 negative samples 


The goal is to aim for a high precision (as little false negatives as possible).   

How do I go on about choosing the weights for the random forest? 

I tried to balance out the y_train ratio by assigning weight 1 to "negative" and 173 to "positive", but that still caused all the samples to be assigned to negative.

At this point, should I already consider this a problem with the features used, or should I try to assign higher weights with GridSearch (roc_auc as scoring parameter) and set the decision-threshold higher first?
I totally missed out on the fact, that the RandomForestClassifier accepts the values “balanced” and “balanced_subsample” to automatically assign the weights as per documentation