Introduction to Supervised Machine Learning

The topics of supervised and unsupervised machine learning are up and coming in today's age, and both are essential to understand for those of us invested in the data analytics world. Below are two quick definitions for the differing types of machine learning.

Supervised Machine Learning is the process of learning the relationships between input data based on pre-existing knowledge, descriptors, and models to classify future unknown data in a more accurate way.

Unsupervised Machine Learning is the process of conceptualizing relationships and input data on the fly with the intent to understand, infer, and predict a balanced structure within a set of current or future data.

While both tactics for machine learning have their advantages and disadvantages, supervised machine learning tends to be utilized more frequently do to having an overall better comparative performance. Supervised machine learning is used throughout many fields of data analytics, a couple of examples being text analysis, sentiment analysis, clustering, risk analysis, and much more! 

While supervised machine learning has many benefits, it has a few shortcomings as well, one of them being a reliance on labeled, network data for testing purposes. Fortunately, ExactData combines our synthetic data with Ixia's network traffic generator to counteract these shortcomings and test both frequently and rigorously to ensure the proper training of data models using supervised machine learning capabilities.

More on this subject can be found here in our Supervised Machine Learning white paper!