Parameter Selection: Why We Should Pay More Attention to It
This is an incremental opinion paper addressing the problem of uncertain research progress for machine learning practitioners due to overlooked parameter selection.
The paper highlights that insufficient parameter selection in supervised learning can lead to misleading conclusions, using the example of multi-label medical code prediction where subsequent studies on a popular benchmark may not have surpassed the original approach if proper parameter tuning had been conducted.
The importance of parameter selection in supervised learning is well known. However, due to the many parameter combinations, an incomplete or an insufficient procedure is often applied. This situation may cause misleading or confusing conclusions. In this opinion paper, through an intriguing example we point out that the seriousness goes beyond what is generally recognized. In the topic of multi-label classification for medical code prediction, one influential paper conducted a proper parameter selection on a set, but when moving to a subset of frequently occurring labels, the authors used the same parameters without a separate tuning. The set of frequent labels became a popular benchmark in subsequent studies, which kept pushing the state of the art. However, we discovered that most of the results in these studies cannot surpass the approach in the original paper if a parameter tuning had been conducted at the time. Thus it is unclear how much progress the subsequent developments have actually brought. The lesson clearly indicates that without enough attention on parameter selection, the research progress in our field can be uncertain or even illusive.