Parameter

파라미터(Parameter)는 모델 내부에서 결정되는 변수이다. 값은 데이터로부터 결정된다.

A model parameter is a configuration variable that is internal to the model and whose value can be estimated from data.

• They are required by the model when making predictions.
• They values define the skill of the model on your problem.
• They are estimated or learned from data.
• They are often not set manually by the practitioner.
• They are often saved as part of the learned model.

Hyper-parameter

하이퍼 파라미터는 모델링할 때, 사용자가 직접 세팅해주는 값이다. (e.g. learning rate) 하이퍼 파라미터는 정해진 최적의 값이 없기 때문에, 휴리스틱하게 결정하는 경우가 많다.

A model hyper-parameter 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.
• They are often tuned for a given predictive modeling problem.

파라미터와 하이퍼 파라미터를 구분하는 기준은 사용자가 직접 설정하는지 아닌지이다.

Reference

• https://machinelearningmastery.com/difference-between-a-parameter-and-a-hyperparameter/
• https://bkshin.tistory.com/entry/%EB%A8%B8%EC%8B%A0%EB%9F%AC%EB%8B%9D-13-%ED%8C%8C%EB%9D%BC%EB%AF%B8%ED%84%B0Parameter%EC%99%80-%ED%95%98%EC%9D%B4%ED%8D%BC-%ED%8C%8C%EB%9D%BC%EB%AF%B8%ED%84%B0Hyper-parameter