Decision Tree can be used for implementing regression as well as classification models, however , Linear Regression can be used for regression problem only.
Decision tree can be used for regression, even when there is non linear relationships between the features and the output variable. However, Linear Regression works really nicely when there is linear relationship between features and the output variable.
Decision tree also work well compared to Linear Regression algorithms when there are missing values in the data.
Decision tree can be implemented without converting the categorical values to numerical , however, Linear Regression algorithms requires the categorical values to be converted to numerical values.
