Description: Ordinal classification is a supervised learning task where the categories assigned to instances have a natural order. Unlike nominal classification, where categories are mutually exclusive and have no inherent order, in ordinal classification, the classes can be organized in a hierarchy. This means that, for example, in a rating system, the categories could be ‘poor’, ‘fair’, ‘good’, and ‘excellent’, where each category has a level of quality that can be compared to others. This ordering feature allows ordinal classification models not only to predict the category to which an instance belongs but also to reflect the order relationship among categories. The algorithms used for ordinal classification are often adapted to take this hierarchical structure into account, which can improve prediction accuracy compared to standard classification methods. Ordinal classification is particularly relevant in contexts where decisions are based on ranks or levels, such as satisfaction surveys, risk analysis, and performance evaluation, where the order of categories is crucial for interpreting results.
Uses: Ordinal classification is used in various applications, such as customer satisfaction surveys, where respondents can rate their experience on a satisfaction scale. It is also applied in risk assessment, where risks can be classified as low, medium, or high. In educational contexts, it can be used to classify performance into categories such as insufficient, sufficient, notable, and outstanding. Additionally, in product analysis, it can be employed to classify user reviews into different quality levels, helping consumers make informed decisions.
Examples: An example of ordinal classification is the grading system in educational institutions, where students can receive grades ranging from ‘F’ (failing) to ‘A’ (excellent). Another example is the movie rating system on various platforms, where users can rate movies on a scale of 1 to 5 stars, reflecting their level of satisfaction. It can also be observed in service evaluations, where users can classify their experience into categories such as ‘very dissatisfied’, ‘dissatisfied’, ‘neutral’, ‘satisfied’, and ‘very satisfied’.
