In Chapter 8, Using Graph-Based Features in Machine Learning, we drew the following schema:
The Feature engineering step involves extracting features from our dataset. When this dataset consists of observations that already have numerical or categorical characteristics, it is easy to imagine how to build features from these characteristics.
However, some datasets do not have that tabular structure. In such cases, we need to create that structure before feeding the dataset into a machine learning model.
Take a text, such as a book, for example, that contains thousands of words. Now imagine that your task is to predict, from a given word, which word is more likely to come after. To create this model, we need to find a machine learning model such that:
Model(Word) = Next Word
However, machine learning models, from linear regression to artificial neural networks, work with feature vectors. Therefore, we need to find a list of features associated with...
