Linear Regression vs Random Forest performance accuracy
There for sure have to be situations where Linear Regression outperforms Random Forests, but I think the more important thing to consider is the complexity of the model.
Linear Models have very few parameters, Random Forests a lot more. That means that Random Forests will overfit more easily than a Linear Regression.
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?