What Does Hyperparameter Mean?
A hyperparameter is a machine learning parameter whose value is chosen before a learning algorithm is trained.
Hyperparameters should not be confused with parameters . In machine learning, the label parameter is used to identify variables whose values are learned during training. The prefix hyper is used to identify higher-level parameters that control the learning process.
Every variable that an AI engineer or ML engineer chooses before model training begins can be referred to as a hyperparameter -- as long as the value of the variable remains the same when training ends.
It’s important to choose the right hyperparameters before training begins because this type of variable has a direct impact on the performance of the resulting machine learning model. Examples of hyperparameters in machine learning include:
- Model architecture
- Learning rate
- Number of epochs
- Number of branches in a decision tree
- Number of clusters in a clustering algorithm
