Preparing the Data

In Chapter 3, Getting Started with Neural Networks, we introduced the backpropagation algorithm, which is used by gradient descent algorithms to train a neural network. These algorithms work on numbers and can't handle nominal/categorical input features or class values. Therefore, nominal input features or nominal output values must be encoded into numerical values if we want the network to make use of them. In this section, we will show several numerical encoding techniques and the corresponding nodes in KNIME Analytics Platform to carry them out.

Besides that, we will also go through many other classic data preprocessing steps to feed machine learning algorithms: creating training, validation, and test sets from the original dataset; normalization; and missing value imputation.

Along the way, we will also show you how to import data, how to perform a few additional data operations, and some commonly used tricks within KNIME Analytics Platform. The workflows...