I have a patient dataset on which I trained a RF classifier to predict whether a patient ends up in the hospital or not. Nevertheless, this dependent variable is imbalanced (66% of the patients ended up in hospital and 33% didn't).

I obtained an ROC-AUC score of ~76% which is not too bad, but when I look at the precision and recall (and the confusion matrix) there is something funny going on.

Precision = 0.66 & Recall = 1

Also, looking at the predicted labels, the RF classifier predicted everyone of the test set to end up in the hospital (label 1). Is this because of the imbalance in the dependent variable (hospitalisation)? How to tackle this problem if this were the case?

I have a patient dataset on which I trained a RF classifier to predict whether a patient ends up in the hospital or not. Nevertheless, this dependent variable is imbalanced (66% of the patients ended up in hospital and 33% didn't).

I obtained an ROC-AUC score of ~76% which is not too bad, but when I look at the precision and recall (and the confusion matrix) there is something funny going on.

Precision = 0.66 & Recall = 1

Also, looking at the predicted labels, the RF classifier predicted everyone of the test set to end up in the hospital (label 1). Is this because of the imbalance in the dependent variable (hospitalisation)? How to tackle this problem if this were the case?