Our Modeler records every interaction taken by every user in your system
By combining different models we achieve higher testing accuracy
We use long short-term memory models to predict next steps with 85% accuracy
For machine learning and AI to reach its full potential, other elements of supervised learning are often required to “boost” model performance and optimize results. Boosting is a term where weak models are made into strong models. Adaptive Boosting is a machine learning meta-algorithm that can be used in conjunction with many other types of learning algorithms to improve performance.Nowadays, boosting techniques are used to help solve a wide range of AI problems. For instance here at Functionize, we use an autonomous intelligent test agent to run all your automated tests. This test agent uses multiple forms of artificial intelligence. Many of these rely in turn on boosting. For instance, our ML engine uses computer vision as one way to identify and select elements on the screen.
Functionize is the industry’s most advanced enterprise AI-powered testing. We help teams break through testing barriers and enable organizations to release faster.
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