Machine learning is a broad field with a variety of approaches to addressing a gamut of tasks. In this article, we will describe some of the commonly addressed tasks using machine learning. We will also comment and point to suitable approaches for handling such tasks.
Classification is the task of assigning categories (or classes) to given instances automatically. The machine learning model that has been trained to achieve such a goal is known as a classifier. Classification falls in the realm of supervised learning — the sub-field of machine learning that enables models to be trained by observing labeled or supervised examples. For example, to learn a classifier to identify spam emails, each supervised example will be a tuple consisting of the email information (text, subject, from, to) and its category (spam or no spam).
Depending on the number of categories and their relationships, classification problems fall into several types.
Binary classification: An instance must belong to exactly one among two categories. The classifier itself is known as a binary classifier.
Multi-class classification: An instance must belong to exactly one among many (more than two) categories. In a multi-class scenario, the categories are mutually exclusive.
Multi-labeled classification: An instance may simultaneously belong to more than one category from among several categories. Thus, in a multi-labeled set up, the categories are not mutually exclusive.
