Regression Model Evalation with unbalanced target; alternative to R-squared?

The Data look something like this:

[0, ...., 0, 2.8, 10, 4.5,]

So there are a lot of zeros to predict. And just a few non zeroes.

Apllying R2 to a potentially predictive model for this kind of Data is not feaseable: The difference between all zero prediction and the expected values is not that big.

Is there a R2-like metric to aid this situation ?

• What goes wrong when you use $R^2$ despite your reservations? You’re still seeing how much variability you’re regression accounts for compared to always guessing the mean of the response variable.
– Dave
Apr 2, 2020 at 11:28
• I run my model with simple R^2 , yet the obtained R2 fails to convey an improvement while tuning hyperparameters. Apr 2, 2020 at 12:52
• So your model is doing a poor job of predicting. That’s an issue with your model, not $R^2$.
– Dave
Apr 2, 2020 at 13:03
• that why i want to tune the model into a better predicting model through hyperparameters. But for this, the metric for model evaluation has to convey the right message. R2 failed for me in this case Apr 2, 2020 at 16:39
• Why is $R^2$ failing you by (seemingly correctly) reporting a poor model fit?
– Dave
Apr 2, 2020 at 16:44