After parameter tuning, we can now run the model for maximum performance. In order to do so, we will make a few important changes to the model options. Ahead of making the changes, let's have a more in-depth review of the model options:
- hidden_node: These are the number of nodes in the hidden layer. We used a looping function to find the optimal number of nodes.
- out_node: These are the number of nodes in the output layer and must be set equal to the number of target classes. In this case, that number is 2.
- out_activation: This is the activation function to use for the output layer.
- num.round: This is the number of iterations we take to train our model. In the parameter tuning stage, we set this number low so that we could quickly loop through a number of options; to get maximum accuracy, we would allow the model to run for more rounds while...
