Today, machine learning has become an integral part of our lives. It is used in a wide variety of applications, from healthcare to finance and from transportation to gaming. However, up until now, one of the major challenges with machine learning has been ensuring the quality of the data used to drive decisions.

A team of researchers from the University of California, Santa Barbara have developed a new system which aims to improve the quality of machine learning data. The team has developed an algorithm which can detect errors in data, as well as inconsistencies or anomalies, and then correct them before they are used to make decisions. The system is based on an innovative approach to machine learning which combines traditional rule-based methods with more modern statistical methods. This new approach allows the system to detect errors in data more accurately and efficiently, making machine learning more reliable and accurate.

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source: Phys.org