Description: Hyperparameters are parameters whose values are set before the learning process begins in machine learning. Unlike model parameters, which are learned from data during training, hyperparameters are configurations that affect the behavior of the learning algorithm. These can include the learning rate, the number of layers in a neural network, the batch size, among others. The proper choice of hyperparameters is crucial, as it can significantly influence the model’s accuracy and efficiency. Adjusting these values may require a trial-and-error process, and techniques such as grid search or Bayesian optimization are often used to find the optimal combination. In the context of data querying and manipulation, although the term ‘hyperparameter’ is not conventionally used, it can be related to the configuration of queries and functions that affect performance and output, similar to how hyperparameters affect the performance of machine learning models.
