ABSTRACT
The term machine learning was coined in the late 1950s to label a set of inter-related algorithmic techniques for extracting information from data without human intervention. There are two main branches in machine learning: supervised learning and unsupervised learning. The basic goal of supervised learning is to find a function that accurately describes how different measured explanatory variables can be combined to make a prediction about a response variable. Classifiers are an important complement to regression models in the fields of machine learning and predictive modeling. Whereas regression models have a quantitative response variable, classification models have a categorical response. In unsupervised learning, the outcome is unmeasured, and thus the task is often framed as a search for otherwise unmeasured features of the cases. A decision tree is a tree-like flowchart that assigns class labels to individual observations.
