What is classification?

This article explains the concept of Classification as a Machine Learning term and as a concept within the Engine.

The need for classification is everywhere

Some examples are:

• Predicting what type of failure a machine will suffer over time given the history of its sensor readings,

• Predicting whether a customer is going to churn in the next month given their recent account activity,

• Predicting what kind of mobile phone a customer is going to purchase given their demographic characteristics, and

• Predicting whether a person is going to have diabetes given their lifestyle.

Businesses often require classification to be automated in a real-time manner and at a large scale. For example, banks may have several thousand customers using their credit cards every minute. In this case, every transaction will need to be classified as either normal or fraudulent, so that the system can take immediate preventive action such as asking the customer for password verification.

Machine Learning can automate these tasks

Machine Learning (ML) provides a way to achieve the automation of such classification tasks. ML Classification Models can be trained using specialized algorithms (or “model templates”) that learn from existing data of observations for which the class (label) to be predicted is known, called labeled data. The resulting trained model can accurately predict the unknown class or label for new data.

Classification within the AI & Analytics Engine 

In the AI & Analytics Engine, you can build state-of-the-art ML classification models from tabular datasets using the App builder pipeline, where you'll need to upload your labeled data and choose the column to predict. You can manually choose the classification algorithm, or have the Engine choose the best based on prediction quality or training time.