What is Classification?
Classification is a feature built into LogicPlum’s automated machine learning platform that predicts the category of given data points.
These categories are also known as classes, targets, or labels.
Classification is one of the most common uses of machine learning and data science.
When classifying data points into categories for a systemic group of observations, machine learning technology performs the following steps:
Why is Classification Important?
There are infinite possibilities for how businesses can leverage machine learning classification in everyday practical ways. For example, spam email detection can be considered a classification problem.
The classifier is trained to understand how input variables relate to the class. Once adequately trained using actual spam and non-spam emails, the classifier can operate automatically.
Spam email is considered a binary classification as it only has two classes – spam and non-spam. Classification can operate on multiclass problems that have three or more possible classes.
For example, your business might want to forecast which of several marketing strategies will convert the most leads based on historical performance. From there, you can focus your marketing budget on the most effective approach.
When you understand which specific buyers are responding to which messages and what the perfect method of delivery is for each, you optimize your marketing investment to reach your ideal buyer.
LogicPlum and Classification
LogicPlum’s automated machine learning platform includes classification algorithms that automatically recognize whether your target variable is suitable for classification or requires regression analysis that will estimate a numeric value instead.
Unlike other providers, where classification can be an enigmatic process that is unclear about which characteristics were most influential, Logic Plum provides clarity about the specific factors used to determine classification.
These prediction explanations provide insight and a better understanding of the end product, which can empower you to more easily justify the investment in technology to management and explain the outcomes to regulatory agencies.