MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -A model hyperparameter is a configuration that is external to the model and whose value cannot be estimated from data. They are often used in processes to help estimate model parameters. They are often specified by the practitioner. They can often be set using heuristics.

Detailed explanation-2: -Hyperparameter tuning is an essential part of controlling the behavior of a machine learning model. If we don’t correctly tune our hyperparameters, our estimated model parameters produce suboptimal results, as they don’t minimize the loss function. This means our model makes more errors.

Detailed explanation-3: -Hyperparameters in Machine learning are those parameters that are explicitly defined by the user to control the learning process. These hyperparameters are used to improve the learning of the model, and their values are set before starting the learning process of the model.

Detailed explanation-4: -There is no answer to how many layers are the most suitable, how many neurons are the best, or which optimizer suits the best for all datasets. Hyperparameter-tuning is important to find the possible best sets of hyperparameters to build the model from a specific dataset.