Supervised Machine Learning

What is Supervised Machine Learning?

Supervised Machine Learning refers to a method of developing a predictive function by using a training set of labeled examples that pair input data with labeled output. Once the function optimizes how it associates certain input values with labeled outputs, the function can be tested with additional data to validate its accuracy in predicting the right label. With sufficient accuracy levels from those training and validation runs, the function can then be applied operationally to new input data to create output labels. It is important to measure, monitor, and adjust the predictive function over time as input data may shift or the algorithm may have an opportunity to improve its accuracy using a longer history, additional inputs, or a different approach.


Why is Supervised Machine Learning important?

Supervised Machine Learning is important for automating tasks that need to be performed at a scale or speed that is too challenging or expensive for humans to perform, particularly for use cases like image recognition and speech translation. Creating an effective predictive function depends on having a full set of representative data that is accurately labeled, as well as labels that can be sufficiently differentiated using the available data.


How Enables Organizations to Use Supervised Machine Learning makes it easy to apply different machine learning models to address domain-specific applications of AI to deliver business value today. The C3 AI® Suite is a complete, end-to-end platform for designing, developing, deploying, and operating enterprise AI applications at industrial scale. The ML Pipelines feature enables data scientists to easily build pipelines of supervised ML models to create complex analytic workflows, combining techniques from different domains to produce a more sophisticated result. For example, one could use image recognition to translate a complex diagram into its constituent components and labels, then match the labels to a database bill of materials, connect to available sensor data, build a model to predict potential failures hours or days in advance, and create work orders for provisioning replacement parts to address the issue during a maintenance window. also delivers a portfolio of prebuilt, SaaS enterprise AI applications for a growing number of use cases such as predictive maintenance, inventory optimization, fraud detection, anti-money laundering, and more. Some of the world’s largest organizations – such as Shell, US Department of Defense, Enel, Koch Industries, and others – use technology to drive digital transformation initiatives that significantly reduce costs, increase asset availability and reliability, improve human safety, and enhance customer satisfaction.