Entropy

Hyperparameter Tuning and Optimization

Hyperparameters are model-specific settings that control how learning occurs. Unlike model parameters, which are learned from data, hyperparameters must be manually tuned for optimal performance. Techniques such as Grid Search, Random Search, and Bayesian Optimization are commonly used for hyperparameter tuning.

Fine-tuning parameters like learning rate, batch size, and regularization helps prevent overfitting and improves accuracy. Automated tools like Optuna and Hyperopt further enhance the efficiency of this process.