Here is where i come from: If you train your machine on a dataset with too few input variables, you cannot capture the behaviour. (Current analogies may be corporations focused on "indicators" and governments focused on GDP.) When learning statistics, we are taught of the perils of #overfitting: If the number of input variables is similar to the number of data points, your calculations perform better on the training data, but tend to perform worse on out-of-sample data.