Parameters that are not learned from the data but are set by the user before training the model. These parameters control the behavior and performance of the ML algorithm. Here are some commonly used hyperparameters in ML:
- Learning rate
- Number of hidden layers
- Number of neurons per layer
- Activation functions
- Regularization parameters
- Batch size
- Dropout rate
- Number of iterations or epochs