Fig. 5.

Effect of training-set size and sheet size on test accuracy. (A) With fewer distinct training examples, training accuracy is high, but test accuracy is low (overfitting). Increasing the number of training examples improves test accuracy, at the expense of training accuracy. (B) Sheets with more creases show larger improvements in test accuracy with increasing number training examples, as expected of complex models with more fitting parameters. (C) A small, untrained sheet (13 creases) shows $\sim 10$ folded structures (color coded) in response to different force patterns. A larger sheet (49 creases) shows $\sim 400$ folded structures instead, each with smaller attractor regions in the space of force patterns. Consequently, larger sheets can create more flexible classification surfaces by combining smaller attractor regions; such complex models with more fitting parameters require more training examples to avoid overfitting.