Answer: Model hyperparameters are set before training and control the learning process, while model parameters are learned during training and define the mapping from input to output.
Here’s a comparison of the difference between model hyperparameters and model parameters in tabular format:
| Aspect | Model Hyperparameters | Model Parameters |
|---|---|---|
| Definition | Settings or configurations are set before training, controlling the learning process. | Variables learned during training, define the mapping from input to output. |
| Examples | Learning rate, number of hidden layers, batch size. | Weights and biases in neural networks, slope and intercept in linear regression. |
| Tuning | Set manually or through optimization techniques such as grid search or random search. | Automatically learned from training data through optimization algorithms like gradient descent. |
| Role | Influence the behavior and performance of the learning algorithm. | Determine the specific function that the model represents and its ability to make predictions. |
Conclusion:
Model hyperparameters and model parameters play distinct roles in machine learning models. Hyperparameters are set before training and control the learning process, while parameters are learned during training and define the mapping from input to output. Understanding and appropriately setting both hyperparameters and parameters are essential for building effective and well-performing machine learning models.