You are collecting data from more sources now than ever before. Advances in technology are making it possible for you to gain access to that data faster and at reduced cost. But having access to all that data and speed doesn’t necessarily lead to faster, less expensive insights. Before you can study your data, you need to build a framework for it.

When machine learning is combined with taxonomy, your data can be arranged into categories, or fine grained classifications. This gives you the ability to really see the significance in the data set as it relates to the question — or questions —  you’re asking, and helps you make better predictions.

Contextual Machine Learning

Contextual machine learning helps combine the best of machine learning capabilities with a classification system that enriches your data. The addition of a hierarchy brings meaning to the data so you can see the relationships between high-level categories and the sub-classifications underneath them. You can look at broad strokes or very specific, fine-grained categories to find associations between similar terms, or see how various topics break down by segment in the hierarchy. Structured data helps you make better judgments.

eContext’s classification system is flexible. It provides the top-level categorical view that other similar systems show you, and then it goes very deep, delivering opportunities for classification of your data into 21 tiers. The depth of our system mans you can take a deep dive into your data and arrive at better predictions.

Getting trained

We’ve been working with Seth Grimes of the Alta Plana Corporation, who recently published a white paper titled, “Contextual Machine Learning: It’s Classified“, which outlines various models for this type of machine learning and explains how taxonomy enhances it.

The paper shows how text classification can help people in a wide range of professions arrive at better decisions — from those working at agencies and in marketing to people who are focused on advanced projects such as chat bot applications and or even far-reaching logistics and manufacturing projects.

The paper also relays five ways that better text classification can improve machine learning accuracy. For instance, applying classification for better relevancy and more structured training data sets, or using classification resources to test machine learning outputs for model validation.

Whatever your profession is, it’s safe to say that machine learning will have an impact on what you’re doing at some point. Classification is ideal for anyone who works with a lot of data, so get up to speed on the latest in data trends.