What does it mean when the learning rate is set too high in a machine learning model? How does a high learning rate affect the convergence of the model during training? What issues such as overshooting, instability, or divergence can occur? How can these problems impact model accuracy and performance? What strategies can be used to address or correct a learning rate that is too high?