Description: Hyperparameters are configurations set before starting the training process of a machine learning model. Unlike model parameters, which are adjusted during training, hyperparameters are fixed and determine crucial aspects of the learning process. These can include the learning rate, the number of layers in a neural network, the batch size of data, among others. The proper choice of hyperparameters is fundamental, as it directly influences the performance and effectiveness of the model. A poorly adjusted hyperparameter can lead to overfitting or underfitting, affecting the model’s ability to generalize to new data. In the context of machine learning, understanding and optimizing hyperparameters is essential for developing robust and efficient models in the field of artificial intelligence.
