Your Journey to Supervised Machine Learning [Methodology Framework]

Worldwide investment in advanced analytics is currently over $60 billion a year and is set to grow at a rate of 12.5% each year from 2021 through 2024.

But many AI and analytics investments result in poor business value, impairing an organization’s ability to succeed with customer relations and risk reduction. In order to drive actionable decision-making from AI and machine learning, organizations need to adjust their analytics strategy.  

Through the creation of entities and networks, Quantexa provides the context you need to make better operational decisions at scale. By leveraging AI and machine learning within each capability of its revolutionary Contextual Decision Intelligence (CDI) platform, Quantexa uses network visualization to provide an explainable and transparent approach that enables you to build stronger machine learning models.

In Quantexa’s AI & Analytics methodology framework, you’ll learn: 

• The four core capabilities of Quantexa’s CDI platform leveraging AI and machine learning to deliver better, transparent, and accessible decisions to end-users 

• How you can improve the traditional machine learning workflow with context  
• Which machine learning algorithms Quantexa supports through its open API approach  
• The three key steps to achieving a successful supervised machine learning model   

Discover how Quantexa supports building predictive models by using contextual enrichment to help you build better features. By adding an additional step to enrich data, Quantexa’s CDI technology provides the context needed to deliver greater model uplift than improved machine learning algorithms alone. 

Download Quantexa’s AI & Analytics Methodology to unlock insight to building stronger AI and machine learning models.  

Drive Business Value from Data Using AI and Analytics  

Even before the COVID-19 pandemic, it was clear that for companies to succeed they needed world-class performance in key processes, including customer relations, fraud, and financial crime detection, know your customer, and customer churn analysis. 

But to do these effectively requires new and advanced techniques such as AI and machine learning. 

At least three-quarters of companies are limiting their use of analytics and fail to capitalize on the operational decision-making opportunity of modern data intelligence. 

IDC Report, Maximize Your Decision Intelligence by Analyzing Contextual Data

Supporting the Journey to Better Machine Learning Models  

Using the same underlying data layer, Quantexa supports multiple use cases from a single platform, allowing you to drive better decisions across your organization.  

Using contextual enrichment, Quantexa supports the building of predictive models and delivers greater model uplift than machine learning algorithms alone.  

Supervised vs Unsupervised Machine Learning   

Unsupervised learning provides an exploratory path to view data, allowing organizations to spot patterns in large volumes of data at speed when compared to manual observation. 

The traditional machine learning workflow is central to Quantexa’s approach. However, in use cases such as financial crime, where target cases are relatively rare, feature engineering using business knowledge is critical. 

Your journey to supervised machine learning starts here.