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 ?
Looks like your data are severely unbalanced if they are almost all zeros. Try data balancing or some form of cross-validation. To your original point: there is not a simple R^2 metric fix.