Hyperparameter Tuning Strategies
Choosing the right hyperparameters can make or break a machine learning model. Because the search space is often large, systematic strategies are essential.
1. Grid and Random Search
Grid search exhaustively tests combinations of predefined parameter values. While thorough, it can be expensive. Random search offers a quicker alternative by sampling combinations at random, often finding good solutions faster.
2. Bayesian Optimization
Bayesian methods build a probabilistic model of the objective function and choose the next parameters to evaluate based on expected improvement. Libraries like Optuna and Hyperopt make this approach accessible.
Automated tools can handle much of the heavy lifting, but understanding the underlying strategies helps you choose the best one for your problem and compute budget.
