Massachusetts Institute of Technology researchers examined the issue of shortcuts in a popular machine learning method and proposed a solution forcing the model to use more data in its decision-making to avoid pitfalls. By removing simpler characteristics, researchers can redirect the model’s focus to more complex features of the data that were missed. The researchers then ask the model to solve the task in two ways — first by focusing on the simpler characteristics and second by using the complex features. According to researchers, doing so reduced the occurrence of shortcut solutions and boosted the model’s performance.
Read more : Improving Machine Learning With Big Data-Driven Algorithms.
