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Hyperparameters in machine learning refer to the configurable parameters that govern the learning process of an algorithm. These include settings such as the learning rate, number of epochs, batch size, and the architecture of neural networks. They are not learned from the training data but are instead set prior to the training process.

By adjusting these hyperparameters, one can greatly influence how well the model will perform on unseen data. Finding the right combination of hyperparameters often involves experimentation and techniques like grid search or random search. This flexibility allows practitioners to adapt the learning process to better fit the specific characteristics of the data they are working with, ultimately leading to improved model performance.

In contrast, other answers highlight attributes that do not capture the essence of hyperparameters. Static values and constants do not reflect the dynamic nature of hyperparameters in tuning the model, nor do they accurately describe their role in influencing the learning algorithm's adaptability.