In machine learning, the term hyperparameter refers to those parameters that cannot be learned from the regular training process directly. These are the various knobs that you can tweak on your machine learning algorithms. Hyperparameters are usually decided by training the model with different combinations of the parameters and deciding which ones work best by testing them. Ultimately, the combination that provides the best model would be our final hyperparameters. Setting hyperparameters can have a significant influence on the performance of the trained models.
On the other hand, cross-validation is often used in conjunction with hyperparameter tuning. Cross-validation (also know as rotation estimation) is a model validation technique for assessing the quality of the statistical analysis and results. Cross-validation helps to describe...
