Hyperparameter Tuning in Machine Learning: A Prioritised Approach

Hyperparameters are an essential part of machine learning models and can significantly influence their performance. Tuning these hyperparameters can be quite challenging due to the large search space and the time required to train each model. However, not all hyperparameters are equal, and certain ones tend to have a more substantial impact on the model's performance. In this blog post, we will discuss the order of priority in which we should tune hyperparameters, focusing on learning rate, momentum term beta, the number of hidden units, mini-batch size, the number of layers, and learning rate decay.

Learning Rate:

The learning rate is arguably the most crucial hyperparameter in most machine learning algorithms. It determines the step size at each iteration while moving towards a minimum of a loss function. If the learning rate is set too high, the model might overshoot the minimum, and if it's too low, the model might need too many iterations to converge or might get stuck in a local minimum. Consequently, optimizing the learning rate should be your first priority.

Momentum Term (Beta):

The momentum term, often denoted as 'beta', is a hyperparameter used in optimization algorithms like gradient descent with momentum or Adam. It helps to dampen oscillations and can accelerate the learning process by taking into account past gradients. After setting the learning rate, adjusting the momentum term is a good next step. Typical values range between 0.9 and 0.999.

Number of Hidden Units:

The number of hidden units in a layer controls the capacity of the network. Too few might lead to underfitting (the model is too simple to learn the underlying structure of the data), while too many might lead to overfitting (the model is so complex that it learns the noise in the data). After tuning the learning rate and momentum, you should adjust the number of hidden units.

Mini-batch Size:

The mini-batch size is another significant hyperparameter, especially when training deep neural networks. It can affect the model's performance and speed of convergence. Smaller mini-batch sizes typically converge faster but the final model's performance might have higher variance. Conversely, larger mini-batch sizes provide a more accurate estimate of the gradient, but the training process can be slower.

Number of Layers:

Deep learning models can have varying numbers of layers. More layers allow the model to learn more complex patterns, but they also make the model more prone to overfitting and harder to optimize. After the above hyperparameters are set, you can experiment with adding or removing layers to see if it improves performance.

Learning Rate Decay:

Learning rate decay gradually reduces the learning rate during training. This can help to make the model converge faster and can lead to better final performance. However, it's usually not as impactful as the above hyperparameters and thus should be tuned last.

Conclusion:

Hyperparameter tuning is a critical step in machine learning and can significantly impact a model's performance. While the order listed above is a good starting point, it's important to remember that different problems might require different strategies. The key is to experiment and iterate until you find the best set of hyperparameters for your specific task.