Compare popular search strategies and when to use each for faster, better models. Avoid overfitting to validation data and set up robust tuning workflows.
Why can random search outperform grid search in high-dimensional spaces?
Grid search cannot run in parallel
It explores more unique values per hyperparameter under the same budget
Grid search adapts to results and wastes trials
Random search is guaranteed to find the global optimum
What is the key idea behind Bayesian optimization for tuning?
Increase batch size until loss decreases
Model the objective with a surrogate and select promising points via an acquisition function
Train multiple models and average predictions
Exhaustively try every combination
Which safeguard reduces overfitting to the validation set during tuning?
Use nested cross-validation or a final untouched test set
Increase the number of tuning trials indefinitely
Reuse the same validation fold for both selection and reporting
Pick the configuration with the highest training score
When tuning learning rates or regularization strengths, which scale is usually sensible?
Search only the integers 1 to 10
Fix the value and tune other parameters only
Search on a logarithmic scale
Use a linear scale from 0.0 to 0.1 exclusively
Which method can speed up tuning by cutting poor performers early?
Disabling checkpoints
Successive halving/Hyperband-style early stopping
Reducing the number of folds to one
Always training to full convergence
What’s a practical advantage of random search over Bayesian methods?
It parallelizes trivially without coordination overhead
It automatically de-duplicates tried settings
It guarantees monotonic improvement each trial
It never requires a defined search space
How should the tuning objective be chosen for a business-facing model?
Optimize a metric aligned to the business goal, with constraints if needed
Use whichever metric gives the highest number
Maximize training log-likelihood only
Always optimize accuracy regardless of context
Which configuration reduces variance in tuning results without hiding instability?
Turn off randomness entirely in all libraries
Report only the single best fold’s score
Use a fixed random seed and report variability across folds
Change seeds repeatedly until the best score appears
For tree-based gradient boosting, which parameters are often tuned together?
Learning rate and number of estimators
Batch norm momentum and kernel padding
Dropout rate and image resolution
Embedding size and convolution stride
What is a sensible way to reuse prior tuning knowledge on a new, similar dataset?
Skip validation because results will transfer
Lock parameters to the old best values only
Only test values worse than last time to be safe
Warm-start with past best settings but keep search bounds wide
Starter
You know the basics. Practice with small searches and clear objectives.
Solid
Strong work—mix smarter searches with early stopping and CV.
Expert!
Expert—your tuning balances speed, rigor, and business metrics.
