The key difference between a hyperparameter and a parameter in a machine learning model lies in how their values are determined:
Hyperparameters:
- Set manually: Hyperparameters are specified before the model training process starts. They are not learned from the data itself.
- Control learning process: Hyperparameters define the learning process and influence the model's behavior. They affect the way the model learns from the data and ultimately its performance.
- Tuning: Finding the optimal values for hyperparameters is crucial for achieving good performance. This process is called hyperparameter tuning, which often involves trial-and-error or grid search methods.
- Examples: Learning rate, number of hidden layers in a neural network, regularization strength
Parameters:
- Learned from data: Parameters are the values that the model automatically learns during the training process. They represent the actual knowledge or patterns extracted from the data.
- Define model structure: Parameters determine the specific details of the model's architecture and functionality. They are the "weights" and "biases" that connect the different components of the model.
- Not tuned directly: While hyperparameters are tuned before training, parameters are not directly tuned. They are optimized by the chosen learning algorithm based on the training data.
- Examples: Weights and biases in a neural network, coefficients in a linear regression model
Here's a table summarizing the key differences:
| Feature | Hyperparameter | Parameter |
|---|---|---|
| Set | Manually | Learned from data |
| Controls | Learning process | Model structure and functionality |
| Tuned | Yes | Not directly |
| Examples | Learning rate, number of hidden layers | Weights and biases |
Understanding the distinction between hyperparameters and parameters is crucial for effectively training and optimizing machine learning models. Choosing appropriate hyperparameter values can significantly impact the model's performance, while parameters represent the actual knowledge learned from the data.
