# How to interpret/compare R2 score(s)? [duplicate]

I know that an R² score of 1 is a perfect fit of the model to the truth, a 0 is an constant output regardless of the input, and that negative values are possible when the output varies, but there is no correlation with the input. (At least that's how I understand it?)

But... how do I interpret these values? Or: how do I compare two values with eachother? Comparing 0.8 with 0.9 is obvious: the 2nd value is better than the first. But how to compare low values with negative values?

For example, I have this graph: The green curve is the measurement, while the blue curve is the prediction. I'd expect the R² score to be very low, because it clearly isn't a good prediction, but... turns out, it is a negative value: -1.19.

How do I know which is better? A score of 0.19? Or a score of -1.19?

considering a score of 0 means a constant (wrong) value, then -1.19 must be better than 0?

Follow-up Question: I'm working on a project where I compare several techniques by their R²-score. I was planning on drawing a bar-plot with all the results as an overview. How would I do this, when -1.19 is better than 0, but 1 is best?

Follow-up Question 2: Maybe I'm using the wrong type of metric for this kind of comparison? Am I?

• I use R-squared (R2) to tell me how much of the data variance is explained by the regression model, calculated as "R2 = 1.0 - (regression_error_variance / dependent_data_variance)" . If the model fits perfectly through all data points and that means all errors are zero, and the error variance is zero because all errors are equal value - in a perfect fit R2 = 1.0 - (0.0 / dependent_data_variance) or just 1.0. Using this calculation, R-squared can only be negative if the error variance is greater than the dependent data variance which would tell me that the model is horribly bad. – James Phillips Apr 12 at 21:28
• – mkt - Reinstate Monica Aug 16 at 19:29 