It may seem counterintuitive, but that's a relatively common result in machine learning. Often the issue isn't so much the quantity of data as the representativ

It may seem counterintuitive, but that's a relatively common result in machine learning. Often the issue isn't so much the quantity of data as the representative nature of your training data used to create the model. All other things being equal, a larger set of training data is likely to be more consistent with the true data. Caveats abound, but that's the general idea. One way to think about it is to look at problems with human perception like forced perspective. It's relatively easy to create a situation where the only available information results in mental models that describe the size of an object incorrectly. Given a different point of view (i.e. more information) the faults in the model become obvious.