Neural Network Capacity Explorer
Train a small tabular MLP and watch training AUC climb with capacity while internal test AUC refuses to follow.
- neural network
- overfitting
- capacity
- generalization gap
Training AUC
0.833
Internal test AUC
0.802
External test AUC
0.744
Training loss
Generalization gap
Capacity sweep: hidden units vs. AUC
TrainingInternal test
Model comparison
| Dataset | AUC |
|---|---|
| Training | 0.833 |
| Internal test | 0.802 |
| External test | 0.744 |
AUC on internal and external test sets.
Capacity is not the bottleneck
With roughly a hundred training cases and eight tabular predictors, increasing hidden units drives training AUC toward 1.0 while internal test AUC stays flat or drops. This gap — the generalization gap — is the single most important diagnostic in deep learning. No combination of learning rate, epochs, or regularization manufactures signal that the data do not contain; deep networks earn their keep on raw voxels with thousands of studies, not on a handful of tabular features.