Description: Hyperparameter selection is the process of choosing the best hyperparameters from a set of candidates to optimize the performance of a machine learning model. Hyperparameters are configurations set before the model’s training and are not learned directly from the data. These can include parameters such as learning rate, number of layers in a neural network, batch size, and others. Proper selection of these hyperparameters is crucial, as they can significantly influence the model’s accuracy and generalization capability. Poorly tuned hyperparameters can lead to overfitting or underfitting, negatively affecting the model’s performance on unseen data. There are various techniques for hyperparameter optimization, such as random search, grid search, and more advanced methods like Bayesian optimization. Hyperparameter selection has become an active research area, given the increasing use of complex models in various applications, including fields such as computer vision and natural language processing. In summary, hyperparameter selection is an essential component in developing effective and robust machine learning models.
