Classifying Objects

The classification problem is about identifying the category an object belongs to. In this context, an object is a data item and is fully represented by an array of values (known as features). Each value refers to a measurable property that makes sense to consider in the scenario under analysis. It is key to note that classification can predict values only in a discrete, categorical set.

Variations of the Problem

The actual rules that govern the object-to-category mapping process lead to slightly different variations of the classification problem and subsequently different implementation tasks.

Binary Classification. The algorithm has to assign the processed object to one of only two possible categories. An example is deciding whether, based on a battery of tests for a particular disease, a patient should be placed in the “disease” or “no-disease” group.

Multiclass Classification. The algorithm has to assign the processed object to one of many possible categories. Each object can be assigned to one and only one category. For example, classifying the competency of a candidate, it can be any of poor/sufficient/good/great but not any two at the same time.

Multilabel Classification. The algorithm is expected to provide an array of categories (or labels) that the object belongs to. An example is how to classify a blog post. It can be about sports, technology, and perhaps politics at the same time.

Anomaly Detection. The algorithm aims to spot objects in the dataset whose property values are significantly different from the values of the majority of other objects. Those anomalies are also often referred to as outliers.