What is “hyperparameter tuning” in machine learning?
a) The process of optimizing the parameters that control the learning process
b) The process of adjusting the weights of a neural network
c) A method to preprocess input data
d) A method used to visualize model performance
Answer:
a) The process of optimizing the parameters that control the learning process
Explanation:
Hyperparameter tuning refers to the process of selecting the best values for the hyperparameters of a machine learning model. Hyperparameters are parameters that are not learned during training, such as learning rate, number of hidden layers, and batch size.
Tuning these hyperparameters is crucial for improving model performance, as different hyperparameter settings can significantly impact the model’s ability to learn and generalize. Common methods for hyperparameter tuning include grid search, random search, and Bayesian optimization.
By carefully selecting the right hyperparameters, the model can achieve better results and avoid issues like overfitting or underfitting.