Why is the learning rate needed and what does it affect?

Learning rate — this is a hyperparameter in machine learning algorithms, especially in optimization methods such as gradient descent. It determines how much the model parameters are updated at each training step.

If the learning rate is too high, the model may "overshoot" the minimum of the error function and fail to converge. If it is too small, training will be very slow and may get stuck in local minima.

Example: when updating neural network weights

weights = weights - learning_rate * gradient

Here, learning_rate controls the size of the step in the direction of error reduction. Choosing the right learning rate is important for effective and stable model training.