buildOne report1 publisher Guessing 'no' for every patient scored 95.3% accuracy on public hypothyroid data
Default boosted trees came within half an AUC point of a 200-fit hyperparameter search on five of six public datasets, a dev.to benchmark found. Anyone reviewing an AI-generated modelling notebook can check the cheap baselines and the default trees first, and treat the tuning search as a cost it has to justify.
Reality
- Evidence45
- Adoption
- Not scored
- Hype gap+5
- Incentives
- Not scored
- Confidence55
buildConfirmed2 publishers Google and DeepMind's method records a live search, replays thousands of exploration policies against the stored results, and sends only the winner into the next run. No weights are retrained.
Reality
- Evidence55
- Adoption12
- Hype gap+20
- Incentives68
- Confidence58
buildOne report1 publisher Isolation Forest scores anomalies by counting random cuts on 256 sampled points
The algorithm fits nothing and reads 256 rows per tree, so running it is cheap. The whole judgement call still collapses into one contamination number that no statistic will pick for you.
Reality
- Evidence46
- Adoption
- Not scored
- Hype gap−8
- Incentives22
- Confidence60
buildOne report1 publisher SageMaker v3 drops the framework estimators, and your training code is the migration
One ModelTrainer and one ModelBuilder replace the SKLearn, PyTorch and XGBoost classes, with source directories synced at job launch. The container is now yours to supply.
Reality
- Evidence58
- Adoption20
- Hype gap+24
- Incentives86
- Confidence62
buildOne report1 publisher Optuna killed 60% of the trials and gave back 28% of the time. The gap is in the config.
A pruning demo cut a 20-trial study from 13.0 to 9.4 seconds while abandoning 12 trials. The distance between those two numbers is a ceiling the pruner settings impose.
Reality
- Evidence42
- Adoption
- Not scored
- Hype gap+8
- Incentives34
- Confidence55
buildOne report1 publisher Pick a log anomaly detector on volume, latency and secrets, not on which one is smarter
A practitioner writing on dev.to puts three constraints ahead of the Isolation Forest vs GPT-4o comparison: GB per day, your paging budget, and what your stack traces contain.
Reality
- Evidence34
- Adoption17
- Hype gap+8
- Incentives27
- Confidence38