I have a dataset of shape ca.(4800, 350). Both the dataset X as well as the response y is very sparse (ca. 3500 samples with y=0). I wanted to take a look at the learning curve to estimate the bias-variance tradeoff but I have difficulties to interpret the results.
In my opinion the model mostly learns the y=0 response which then leads to a higher error if the samples with y!=0 are learned (these are the peaks). If that is the case can a over-sampling strategy (e.g. SMOTE) help?
EDIT: per commenter (@user2974951) request a histogram of y: