Understanding the Distinction Between Hyperparameters and Parameters in Machine Learning and Neural Networks

Machine learning (ML) practitioners often find themselves struggling with the concepts of hyperparameters and parameters. This article aims to clarify these terms and highlight their differences, as well as the importance of tuning hyperparameters in the development of efficient and accurate neural networks.

What Are Parameters in Machine Learning?

Parameters, in the context of machine learning, are internal variables that are adjusted during the training process to optimize the performance of a model. These include weights and biases in neural networks. Weights represent the strength of connections between neurons, influencing how strongly the network responds to inputs. Biases, on the other hand, are added to the weighted sum of inputs to ensure that neurons can represent a wide range of output patterns.

What Are Hyperparameters in Machine Learning?

Hyperparameters, in contrast to parameters, are defined as values that are set before the training process begins. They are manually specified by the user and serve as control knobs to adjust the learning process. Examples of hyperparameters include the learning rate, batch size, and the number of layers in a neural network. Unlike parameters, hyperparameters are not directly adjusted during the training process.

Manual Specification and Tuning

Hyperparameters are crucial because tuning them can significantly affect the performance of a machine learning model. For instance, the learning rate significantly influences how quickly the model converges to an optimal solution. A high learning rate can cause overshooting, while a low learning rate may result in very slow convergence. Therefore, manually setting and tuning hyperparameters is an essential part of the machine learning pipeline.

Types of Hyperparameters

There are various types of hyperparameters, each serving different purposes in the model training process. Some common types include:

Learning Rate: Controls the step size during gradient descent. Batch Size: Determines the number of samples processed before the model’s parameters are updated. Number of Layers: Defines the architecture of the neural network. Regularization Parameters: Used to prevent overfitting.

Tuning Hyperparameters

Tuning hyperparameters is a critical step in the development of machine learning models. The process often involves a technique known as k-fold cross-validation. This method splits the dataset into k folds, iteratively using k-1 folds for training and the remaining fold for validation. This process is repeated k times, each time using a different fold for validation. By averaging the performance across these iterations, the stability and robustness of the model can be ensured.

Modern Probabilistic Methods

In more modern probabilistic methods, hyperparameters are often maximized over the marginal likelihood, or hyperparameters can be given hyperpriors to be estimated as part of the inference process. This approach leverages Bayesian methods to provide a more principled way of handling uncertainty in hyperparameters.

Conclusion

The distinction between hyperparameters and parameters is clear: hyperparameters are manually specified values that control the learning process, while parameters are those that are learned during training. Tuning hyperparameters is crucial for the optimal performance of machine learning models, whether traditional or modern probabilistic. Understanding and effectively tuning hyperparameters can significantly enhance model accuracy and generalization.

Keywords

hyperparameters parameters machine learning neural networks tuning