Classification

What is Classification?

Classification is a technique by which you determine to what group a certain observation belongs, such as when biologists categorize plants, animals, and other lifeforms into different taxonomies. It is one of the primary uses of data science and machine learning.

In order to determine the correct category for a given observation, machine learning technology does the following:

  1. Applies a classification algorithm to identify shared characteristics of certain classes.
  2. Compares those characteristics to the data you’re trying to classify.
  3. Uses that information to estimate how likely it is that observation belongs to a particular class.

Why is Classification important?

There are many practical business applications for machine learning classification. For example, if you want to predict whether or not a person will default on a loan, then you need to determine if that person belongs to one of two classes with similar characteristics: the defaulter class or the non-defaulter class. That way, you know how likely the person is to be a defaulter and can adjust your risk assessment accordingly.

Classification problems are not limited to binary cases – multiclass problems have three or more possible classes. For example, you may want to predict which of five (or even more) marketing channels will have the highest return on investment based on historical customer behavior, so that you can optimize your marketing budget by focusing on the most effective channels.

Classification + DataRobot

The DataRobot automated machine learning platform includes a number of classification algorithms and automatically recognizes whether your target variable is a categorical variable suited for classification or a continuous variable that is better suited for regression. Furthermore, DataRobot’s various tools allow you to examine the performance of classification models for both binary and multiclass problems.

Classification

A major drawback to any classification algorithm is the tendency to be a “black box” – that is, to put observations into categories without providing information as to which characteristics were influential in making that determination. DataRobot’s Prediction Explanations feature gives insight into exactly which factors lead to the classification of observations, which allows you to better understand how the model arrived at its outcomes and more easily justify them to both management and regulatory agencies.