Since we have a regression problem, we now know why we chose RMSE, and we have a baseline metric of performance, we can begin to work on improving our model. Every model will have its own different way of improving results; however, we can generalize slightly. Feature engineering helps to improve model performance; however, since this type of work is less important with deep learning, we will not focus on that here. Also, we have already used feature engineering to generate our date and time parts. In addition, we can run our model for longer at a slower learning rate and we can tune hyperparameters. In order to find the best values using this type of model improvement method, we will use a technique called grid search to look at a range of values for a number of different fields.
Let's search for the optimal number of rounds. Using the cross-validation...
