Data-engineering workflow
Model Evaluation
Measuring honestly how well a model performs — its accuracy, completeness, and purity, whether its confidences are calibrated, and how it behaves on data unlike its training set. Careful evaluation is what separates a genuinely useful model from one that has merely memorised its examples.
Highlights
- ›Completeness, purity, calibration — and guarding against overfitting
Methods, data & targets
Used across astro-ML
Knowledge connections
- Associated withBenchmark Datasets
- Associated withBenchmark Datasets
- Associated withTraining Datasets
Sources
The primary and reference sources this topic draws on.
- NASANational Aeronautics and Space Administration
Mission data, planetary science, space telescopes, and public-domain imagery.
Most NASA-produced imagery is in the public domain; individual items are checked for usage terms before publication.