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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.
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.
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
Techopedia Explains Hyperparameter
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.
The process of choosing which hyperparameters to use is called hyperparameter tuning. The process of tuning may also be referred to as hyperparameter optimization (HPO).
