In machine learning (ML), the learning rate is a hyperparameter that determines the step size for updating model parameters during training. It is a critical factor in the optimization process and can have a significant impact on the performance of the model.

The size of the steps taken by the optimization method to update the model parameters is determined by the learning rate, which is usually chosen before training begins. If the learning rate is too high, the model's parameters may be updated too quickly, which may cause it to deviate from the ideal solution and exhibit unstable or oscillatory behavior. If the learning rate is too low, the model's parameters may be updated too slowly, which may hinder convergence and require more training iterations to achieve optimal results.

How to determine the learning rate for a machine learning model?

Determining the ideal learning rate for a particular model and dataset can be difficult, and this process often involves some trial and error. A typical approach is to try various learning rates and evaluate the performance of the model at each stage to find the best learning rate. By dynamically adjusting the learning rate during training using strategies such as learning rate scheduling, model convergence and optimization can be enhanced.

Choosing the right value can have a significant impact on the performance and convergence of your model, which makes the learning rate a critical hyperparameter in machine learning.

References

【1】https://encord.com/glossary/learning-rate-definition/