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?