Machine Learning Hyperparameter Tuning with Grid and Random Search
Summary
This article explains the difference between model parameters, which are learned from training data, and hyperparameters, which are set before training and guide the learning process or model structure. Examples include learning rate, training epochs, nearest-neighbor count, tree depth, and neural-network hidden units. It frames tuning as searching for hyperparameter settings that improve a model’s evaluated performance.
The practical workflow is to choose a model, define a search space, select a search method, use cross-validation, and compare model scores. Grid search tests every specified combination and can systematically cover a small space, but becomes costly as combinations and validation folds increase. Random search samples a fixed number of settings, making it faster in larger spaces while potentially missing better configurations; increasing the sample count raises its coverage and cost. The article offers general guidance rather than trading-specific experiments or evidence that a particular tuning method improves live results. It also notes that financial strategy tuning requires careful backtesting to avoid fitting historical noise and data leakage.
Key ideas
- Model parameters are learned from data, while hyperparameters are chosen to control learning or model structure.
- Grid search evaluates every specified combination and can be computationally expensive.
- Random search samples a subset of settings to reduce computation, with less certainty of finding the best combination.
- Cross-validation is part of the proposed workflow for comparing hyperparameter settings.
- Tuning a trading model should be paired with careful backtesting to limit overfitting and data leakage.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.