Classification
What is Classification?
Classification is a systematic grouping of observations into categories, 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:
- Applies a classification algorithm to identify shared characteristics of certain classes.
- Compares those characteristics to the data you’re trying to classify.
- 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, you need to determine if that person belongs to one of two classes with similar characteristics: the defaulter class or the non-defaulter class. This classification helps you understand how likely the person is to become a defaulter, and helps you 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 achieve the highest return on investment based on historical customer behavior so that you can optimize your marketing budget to focus on the most effective channels.
Classification + DataRobot
The DataRobot AI platform includes a number of classification algorithms and automatically recognizes whether your target variable is a categorical variable that’s suitable for classification or a continuous variable that is suitable for regression. Furthermore, DataRobot’s various tools allow you to examine the performance of classification models for both binary and multiclass problems.
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. The DataRobot Prediction Explanations feature gives you insight into exactly what factors led to the classification of observations. These prediction explanation insights provide a greater understanding of the models’ outcomes, which allows you to more easily justify the outcomes to both management and regulatory agencies.