ANN is defined as a framework for many different machine learning algorithms to work together and process complex data inputs, which “learn” to make predictions by “training”, without being programmed with any task-specific rules.
ANN allows deep learning (multiple layers) and multiple neurons (nodes) to “learn” about the interactive effects of these variables on housing prices. Yet, this Boston housing-price example is not perfect as it still requires human beings to pre-set these 13 explanatory variables, which may result in omission bias and specification bias.