Does it make sense to use sample_weights for balanced datasets?
ANSWER
After a couple rounds of testing and more research, I've discovered that yes, it does make sense to add more weight to the 0's with a balanced binary classification dataset, if your goal is to decrease the chance of over-predicting the 1's. I ran two separate training sessions using a weight of 2 for 0's and 1 for the 1's, and then again vice versa, and found that my model predicted less 1's when the weight was applied to the 0's, which was my ultimate goal.
In case that helps anyone. 
Also, I'm using SKLearn's Balanced Accuracy scoring function for those tests, which takes an average of each separate class's accuracy.
I have limited knowledge about sample_weights in the sklearn library, but from what I gather, it's generally used to help balance imbalanced datasets during training. What I'm wondering is, if I already have a perfectly balanced binary classification dataset (i.e. equal amounts of 1's and 0's in the label/Y/class column), could one add a sample weight to the 0's in order to put more importance on predicting the 1's correctly?
For example, let's say I really want my model to predict 1's well, and it's ok to predict 0's even though they turn out to be 1's. Would setting a sample_weight of 2 for 0's, and 1 for the 1's be the correct thing to do here in order to put more importance on correctly predicting the 1's? Or does that matter? And then I guess during training, is the f1 scoring function generally accepted as the best metric to use?
Thanks for the input!