Description: Hyperparameter estimation is the process of determining the optimal values for hyperparameters in machine learning models. Hyperparameters are configurations set before the model training that influence its performance and generalization capability. Unlike model parameters, which are adjusted during training, hyperparameters must be selected in advance and can include elements such as learning rate, number of layers in a neural network, batch size, and regularization. Proper selection of these values is crucial, as it can significantly affect the model’s accuracy and efficiency. Hyperparameter estimation is often performed using techniques such as grid search, random search, and Bayesian optimization, which allow for exploring different combinations of hyperparameters to find the configuration that maximizes model performance on a validation set. This process not only helps improve model accuracy but can also reduce training time and prevent overfitting, making it an essential part of developing effective machine learning models.
