A new approach in model selection for ordinal target variables
This addresses a lack of suitable tools for model assessment in ordinal prediction, but appears incremental as it builds on existing performance indexes.
The paper tackles the problem of model selection for ordinal target variables by introducing a new performance index that satisfies mathematical properties and is easily computed, showing it discriminates better than existing indexes in toy and simulated examples.
This paper introduces a novel approach to assess model performance for predictive models characterized by an ordinal target variable in order to satisfy the lack of suitable tools in this framework. Our methodological proposal is a new index for model assessment which satisfies mathematical properties and can be easily computed. In order to show how our performance indicator works, empirical evidence achieved on a toy examples and simulated data are provided. On the basis of results at hand, we underline that our approach discriminates better for model selection with respect to performance indexes proposed in the literature.