How do you perform hyperparameter tuning?

Hyperparameter tuning is the process of systematically adjusting the settings that control how a machine learning model learns, with the goal of improving its performance. Hyperparameters are configuration variables set before training, such as learning rates, regularization strengths, or tree depths in decision-based models. Unlike model parameters (like weights in neural networks), hyperparameters are not learned from data and must be manually optimized. The core idea is to test combinations of these settings, evaluate model performance for each, and select the best configuration based on validation metrics like accuracy or loss.

Common methods include grid search, random search, and Bayesian optimization. Grid search exhaustively tests all predefined hyperparameter combinations within a specified range. For example, tuning a support vector machine might involve testing every pair of C (regularization) and gamma (kernel width) values in a predefined grid. Random search, in contrast, samples hyperparameters randomly from distributions, which is more efficient for high-dimensional spaces. Bayesian optimization uses probabilistic models to predict promising hyperparameters based on past evaluations, reducing the number of trials needed. Tools like scikit-learn’s GridSearchCV or libraries like Optuna automate these processes, handling cross-validation and parallelization.

Best practices include starting with a broad search space and narrowing it based on initial results. For instance, when tuning a neural network, you might first test learning rates across orders of magnitude (e.g., 0.1, 0.01, 0.001) before fine-tuning near the best value. Cross-validation is critical to avoid overfitting to a single validation set. However, computational cost is a key challenge: complex models or large datasets may require distributed computing or early stopping to accelerate trials. Developers often prioritize hyperparameters with the highest impact first—like learning rate or network architecture—before optimizing less critical ones. Tools like TensorBoard or Weights & Biases can help visualize results and track experiments. Ultimately, the goal is to balance resource constraints with performance gains, recognizing that marginal improvements may not justify extensive tuning in some cases.