ABSTRACT
Graphics are often interpretable through intuitive inspection alone. They can be used to identify patterns and multivariate relationships in data—this is called exploratory data analysis. Regression models can help us quantify the magnitude and direction of relationships among variables. Thus, both are useful for helping us understand the world and then tell a coherent story about it. The term machine learning was coined in the late 1950s to describe a set of inter-related algorithmic techniques for extracting information from data without human intervention. In unsupervised learning, the outcome is unmeasured, and thus the task is often framed as a search for otherwise unmeasured features of the cases. Classifiers are an important complement to regression models in the fields of machine learning and predictive modeling.
