Linear Regression vs Random Forest performance accuracy
Key advantages of linear models over tree-based ones are:

they can extrapolate (e.g. if labels are between 1-5 in train set, tree based model will never predict 10, but linear will)
could be used for anomaly detection because of extrapolation
interpretability (yes, tree based models have feature importance, but it's only a proxy, weights in linear model are better)
need less data to get good results
have strong online learning implementations (Vowpal Wabbit), which is crucial to work with giant datasets with a lot of features (e.g. texts)
If the dataset contains features some of which are Categorical Variables and some of the others are continuous variable Decision Tree is better than Linear Regression,since Trees can accurately divide the data based on Categorical Variables. Is there any situation where Linear regression outperforms Random Forest?