The ideal learning rate for CatBoost depends on the complexity of the problem and the size of the dataset. In general, a learning rate between 0.01 and 0.1 is a good starting point. If the model is underfitting, you can try decreasing the learning rate, and if it's overfitting, you can increase the learning rate. It's important to perform cross-validation and experiment with different learning rates to find the optimal value for your specific task.