What does the learning rate in machine learning influence?

The learning rate in machine learning plays a crucial role in determining the size of the steps taken during the optimization process, particularly in gradient descent. It dictates how much to change the model parameters in response to the estimated error each time the model weights are updated. If the learning rate is set too high, the optimization process may overshoot the minimum loss function, causing the model to diverge rather than converge. On the other hand, if the learning rate is too low, the learning process can become excessively slow and may get stuck in a local minimum, never reaching the optimal solution.

This sensitive balance makes the learning rate essential for effective model training, as it directly influences how the model learns from the data. Properly tuning the learning rate can enhance the convergence speed and the quality of the final model, which is fundamental in machine learning optimization tasks.