I understand Random Forest models can be used both for classification and regression situations. Is there a more specific criteria to determine where a random forest model would perform better than common regressions (Linear, Lasso, etc) to estimate values or Logistic Regression for classification?
The idea of a random forest model is built from a bunch of decision trees, and it is an supervised ensemble learning algorithm to reduce the over-fitting issue in individual decision trees.
The theory in machine learning is that there is no single model that outperforms all other models and hence, it is always recommended to try out different models before obtaining the optimal model.
With that said, there are preferences of model selection when one is dealing with data of different natures. Each model makes intrinsic assumptions about the data and the model with assumptions that are most aligned with the data generally works better for the data. For instance, logistic model is suitable for categorical data with a smooth linear decision boundary and if the data has this feature whereas a random forest does not assume a smooth linear decision boundary. Hence, the nature of your data makes a difference in your choice of models and it is always good to try them all before reaching to a conclusion.