interpret statistical model metrics

Question

Do you know how to intepret RAE and RSE values? I know a COD closer to 1 is a good sign. Does this indicate that boosted decision tree regression is best?

Answer 1

RAE and RSE closer to 0 is a good sign...you want error to be as low as possible. See this article for more information on evaluating your model. From that page:

The term "error" here represents the difference between the predicted value and the true value. The absolute value or the square of this difference are usually computed to capture the total magnitude of error across all instances, as the difference between the predicted and true value could be negative in some cases. The error metrics measure the predictive performance of a regression model in terms of the mean deviation of its predictions from the true values. Lower error values mean the model is more accurate in making predictions. An overall error metric of 0 means that the model fits the data perfectly.

Yes, with your current results, the boosted decision tree performs best. I don't know the details of your work well enough to determine if that is good enough. It honestly may be. But if you determine it's not, you can also tweak the input parameters in your "Boosted Decision Tree Regression" module to try to get even better results. The "ParameterSweep" module can help with that by trying many different input parameters for you and you specify the parameter that you want to optimize for (such as your RAE, RSE, or COD referenced in your question). See this article for a brief description. Hope this helps.

P.S. I'm glad that you're looking into the black carbon levels in Westeros...I'm sure Cersei doesn't even care.