What Learning Rate Should Your Machine Learning Model Use?

University:
Stanford University
Course:
CS229 | Machine Learning
Academic year:

2023
Views:

65

Pages:

2
Author:

anon

What Learning Rate Should Your Machine Learning Model Use? The selection of learning rate is one of machine learning's most important and continually developing elements. To achieve the optimum results, it is critical to optimize the learning rate, a hyperparameter that controls how rapidly a model learns from its input. We will delve deeply into what the learning rate is, why it's crucial, and how to pick the best one for your machine learning model in this article. What is the rate of learning? The step size at which a model learns from its data is determined by the learning rate, which is a scalar variable. In layman's terms, it establishes, based on the gradient of the loss function, how rapidly a model changes its parameters. The model minimizes the cost function and enhances its predictions by updating the parameters in the direction of the gradient, which is the direction in which the cost function changes the greatest. Why Does the Learning Rate Matter So Much? Because it controls how quickly a model learns from its data, the learning rate is crucial. The model will train slowly and may take a very long time to converge if the learning rate is too low. However, if the learning rate is too high, the model might not converge at all or might converge to a less than ideal outcome. In addition, the model may oscillate and never converge if the learning rate is improperly chosen. How to Pick a Good Learning Rate A key stage in training a machine learning model is selecting the appropriate learning rate, and there are various ways to do this. Generally speaking, it is ideal to begin with a low learning rate and then progressively increase it until the model begins to converge. Plotting the cost function as a function of iterations is a typical technique. If the cost function starts to drop quickly but then oscillates, the learning rate can be too high. In this situation, lowering the learning rate is likely to enhance the model's performance. Another method is to experiment with a range of learning rate values, such as 0.001, 0.01, and 0.1. Plot the cost function as a function of the number of iterations after running gradient descent for a few iterations for each value. The ideal learning rate for your model is probably the one that causes the cost function to decrease with the greatest consistency and speed.