Training and testing the model forms the basis for further usage of the model for prediction in predictive analytics. Given a dataset of 100 rows of data, which includes the predictor and response variables, we split the dataset into a convenient ratio (say 70:30) and allocate 70 rows for training and 30 rows for testing. The rows are selected in random to reduce bias.
Once the training data is available, the data is fed to the neural network to get the massive universal function in place. The training data determines the weights, biases, and activation functions to be used to get to output from input. Until recently, we could not say that a weight has a positive or a negative influence on the target variable. But now we've been able to shed some light inside the black box. For example, by plotting a trained neural network, we can discover trained...
