Why does my random forest classifier predicts one class more often?
While prediction, If you have more date relevant to class 0 obviously this is the expected one.
But if your observation is like, the model predicts more data with class 0 as incorrect, then you model is kind of over-fitted.
To avoid these,

57% and 43% is not kind of class imbalanced, but still you use
class_weight with model.

Feature Engineering helps a lot with these kind of situations.