Hyperparameter Optimization

Hyperparameter optimization is the process of tuning the parameters that govern the training of machine learning models. These parameters, known as hyperparameters, are set before the learning process begins and can significantly affect the model's performance. Common techniques for hyperparameter optimization include grid search, random search, and more advanced methods like Bayesian optimization. This process is crucial in ensuring that models achieve the best possible accuracy and generalization on unseen data. Hyperparameter optimization is widely used in various applications, from deep learning to traditional machine learning algorithms.