Hyperparameter Tuning: Grid, Random & Bayesian Search
Models come with dials you set before training — learning rate, depth, regularization. Tuning is searching those dials for the best validation score, and each try costs a full training run. This topic covers where to look (the search space), how to pick candidates (grid, random, Bayesian/TPE), and how to stop bad candidates early without fooling yourself.
01.The Problem: Same Algorithm, 8% Better Score
You train a gradient-boosted tree model with all the default settings. Validation score: 0.84.
A competitor uses the same algorithm and gets 0.92 on the same data.
What did they do differently?
They turned the dials.
Every model ships with a handful of settings the algorithm cannot learn from data:
- how fast should learning be (learning rate)?
- how deep may each tree grow (max_depth)?
- how hard do we punish complexity (regularization)?
These are hyperparameters — knobs you choose before fitting.
The catch: every try costs a full training run. Minutes, GPU-hours, dollars.
So tuning is really a budget question:
With 100 expensive tries, where do you look, and when do you give up on a candidate early?
Three Samplers, One Evaluation Loop 🔧
Three Samplers, One Evaluation Loop 🔧
All tuning algorithms share the CV-score-and-log loop; they differ only in how the next candidate is chosen and how weak candidates are killed early.
Unlock Topic #73: Hyperparameter Tuning: Grid, Random & Bayesian Search
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