In machine learning, hyperparameters act as the tuning knobs that steer the learning process of a model. Unlike regular parameters learned by the model itself during training, hyperparameters are set by the data scientist beforehand. These values significantly influence the model's performance, making them crucial for optimization.

Key characteristics of hyperparameters:

  • External to the model: Hyperparameters are pre-defined before training and remain fixed throughout the process.
  • Control the learning algorithm: They influence how the model learns from data.
  • Examples: Learning rate, number of hidden layers (in neural networks), batch size.
  • Impact performance: Choosing the right hyperparameters is essential for achieving optimal model performance.

Common examples of hyperparameters:

  • Learning rate: This controls how much the model's weights are updated during training.
  • Number of hidden layers and units: In neural networks, these hyperparameters determine the model's complexity and capacity to learn intricate patterns.
  • Batch size: This defines the number of data samples processed by the model at a time during training.
  • Regularization parameters: These techniques (like L1 and L2 regularization) help prevent overfitting by penalizing the model's complexity, promoting generalizability.

It's important to remember that the specific hyperparameters you encounter will depend on the particular machine learning algorithm you're using. Always refer to the algorithm's documentation to gain a deeper understanding of the available hyperparameters and how to tune them effectively for your machine learning project.