Dropout Must Know Whether the Model Is Training
Dropout changes a model’s forward pass according to module mode. Learn how masking, scaling, evaluation mode and gradient recording affect a reliable inference path.
Wildness 1/5 · Tamed
Topic · 5 stories
model-evaluation
5 stories tagged model-evaluation on AI Tamer, newest first.
Splitting Data After Preprocessing Can Leak the Test Set
Why scaling or imputing before a train–test split can contaminate held-out evaluation, with a training-only pipeline pattern for an audio event classifier.
Wildness 1/5 · TamedA Precision-Recall Curve Answers a Different Question From ROC
A worked prevalence shift shows how the same ROC point can imply very different alert precision, and why ROC and precision-recall curves serve different evaluation decisions.
Wildness 1/5 · TamedAccuracy Can Hide a Classifier That Never Finds the Rare Case
A concrete confusion matrix shows why an impressive accuracy score can conceal zero rare-case recall, and how to compare a classifier with an always-negative baseline.
Wildness 1/5 · TamedQuantization, plainly: what 16-bit, 8-bit and 4-bit models trade
A practical guide to model weight precision, memory estimates, common quantization formats, quality checks and choosing a quant for your workload.
Wildness 3/5 · Partly tamed
