Machine-learning designs can fail when they try to make predictions for people who were underrepresented in the datasets they were trained on.

For instance, a model that anticipates the best treatment choice for somebody with a persistent disease might be trained using a dataset that contains mainly male patients. That design might make inaccurate predictions for female patients when deployed in a healthcare facility.

To improve results, engineers can attempt stabilizing the training dataset by eliminating data points until all subgroups are represented equally. While dataset balancing is appealing, it frequently needs getting rid of big amount of data, harming the design's overall efficiency.

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