Description: Hyperparameter analysis refers to the study of how hyperparameters, which are external parameters to the machine learning model, affect its performance. These hyperparameters can include learning rate, number of layers in a neural network, batch size, among others. Unlike model parameters, which are adjusted during training, hyperparameters must be defined before the training process begins. Proper selection and tuning of these hyperparameters is crucial, as they can significantly influence the model’s ability to generalize to new data. A poorly tuned hyperparameter can lead to issues such as overfitting or underfitting, negatively impacting the model’s accuracy and effectiveness. Therefore, hyperparameter analysis becomes an essential part of the machine learning model development process, where the goal is to optimize performance through techniques such as grid search, random search, and more advanced methods like Bayesian optimization. This analysis not only helps improve model accuracy but can also reduce training time and computational resources required, making the process more efficient and effective.
