SKLearn DT regressor - good enough score? What constitutes as a "good enough" score for a Decision Tree Regressor? The .score() function gives us a general score about our model. This can be 1 if the model predicts all data with a 100% accuracy, and can be arbitrarily worse. If I understand correctly, a score of 0 means the prediction is quasi constant. But starting with what value can we say that the prediction is "usable" (i know this is ambiguous, but still). Is a score of 0.4-0.5 good enough to use the predictions in practice as opposed to not using any machine learning techniques?
The quality of the score depends on the context of the project. 

One approach is a model comparison approach. Define a baseline model. Then see if Decision Tree Regressor is better than the baseline.