⚡ Dropout Rate Optimizer

🔁 Monte Carlo Dropout — uncertainty estimation

Monte Carlo Dropout keeps dropout active during inference. By running multiple forward passes with different dropout masks, you obtain a distribution of predictions. The variance across passes reflects model uncertainty — useful for out‑of‑distribution detection and confidence calibration. This tool simulates the effect of dropout on layer capacity; real MC Dropout requires multiple stochastic passes.