Stop Shipping ML Models With Bare Floats: A Deep Dive Into Statistically Rigorous Model Evaluation (opens in new tab)

Stop Shipping ML Models With Bare Floats Every week, somewhere, a team makes a deployment decision that looks like this: Model A: AUROC = 0.847 Model B: AUROC = 0.851 They ship Model B. Maybe it's better. Maybe it's noise. Nobody knows—because nobody computed a confidence interval. That's exactly why I built reliably-metrics. The Problem With Bare Floats Most ML evaluation today looks like this: print(f"AUROC = {auroc:.4f}") Output: AUROC = 0.8512 Looks precise. Looks scientific. But it tells...