Quantitative/ Model selection
Hyperparameter search
Search configurations under a fair evaluation budget
Hyperparameter search chooses settings not fitted directly by the learner, such as regularisation strength, depth or learning rate. Grid search enumerates a fixed lattice; random search samples a defined distribution; Bayesian optimisation uses previous trials to guide future trials. All require a validation criterion and budget, while final assessment needs untouched test data or outer resampling.
WHEN IT FITS
Use it when plausible configurations materially affect performance and you can afford a transparent search budget, particularly when comparing algorithms whose default settings receive unequal tuning effort.
Strengths
- Replaces undocumented manual adjustment with a reproducible search
- Random or adaptive search can explore influential parameters efficiently
Limitations
- Large searches can overfit validation noise
- Unequal compute budgets can make comparisons misleading
Know the boundary
The best validation trial is not guaranteed to be the best generalising model.
USED ACROSS
Computer science