IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

A well-chosen learning rate plays a pivotal role in the training process of a machine learning model. When the learning rate is set appropriately, it facilitates the model's ability to update weights in a way that progressively leads to lower loss values, thereby effectively converging towards the optimal solution.

An appropriate learning rate allows the model to make consistent updates without overshooting the optimal point in the loss landscape, leading to smoother training and faster convergence. This ensures that the model not only minimizes the loss effectively but also achieves better performance over iterations.

The other alternatives don't accurately represent the benefits of an appropriate learning rate. If the learning rate is too high, the model could oscillate and never settle at the minimum (leading to option A). Conversely, while a very low learning rate might cause the training process to take longer (similar to option C), this is not the primary result of an appropriate setting—it typically leads to effective learning rather than unnecessary delays. Lastly, an appropriate learning rate supports accuracy, contradicting option D, as it enhances the likelihood of reaching higher accuracy through well-tuned weight adjustments.