Hyperparameter Optimization

Hyperparameter optimization refers to the problem of selecting an optimal set of hyperparameters for a learning algorithm. Hyperparameters directly influence the model's adaptability to data, determining whether it will overfit or underfit. By optimizing hyperparameters, one can improve the model's performance under a specific loss function, ensuring that the model has the best assumptions, weights, and training speed for different types of data.