ABSTRACT
This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book discusses supervised machine learning context, to make predictions from text data. It focuses on putting into practice such machine learning algorithms as naive Bayes, support vector machines, and regularized linear models such as implemented in glmnet. The book explores regression models and classification models. The main goal of a predictive model is to generate the most accurate predictions possible. The main purpose of a descriptive model is to describe the properties of the observed data. Many learning algorithms can be used for more than one of these purposes. Concerns about a model's predictive capacity may be as important for an inferential or descriptive model as for a model designed purely for prediction, and model interpretability and explainability may be important for a solely predictive or descriptive model as well as for an inferential model.
