Model assessment for predictive classification models
S Figini, P Uberti - Communications in Statistics—Theory and …, 2010 - Taylor & Francis
S Figini, P Uberti
Communications in Statistics—Theory and Methods, 2010•Taylor & FrancisIn this article, we present a novel methodology to assess predictive models for a binary
target. In our opinion, the main weakness of the criteria proposed in the literature is not to
take the financial costs of a wrong decision into account. The objective of this article is to
derive the optimal cut-off in predictive classification models and to improve model
assessment on the basis of a general class of loss functions. We describe how our proposal
performs in a real application on credit scoring.
target. In our opinion, the main weakness of the criteria proposed in the literature is not to
take the financial costs of a wrong decision into account. The objective of this article is to
derive the optimal cut-off in predictive classification models and to improve model
assessment on the basis of a general class of loss functions. We describe how our proposal
performs in a real application on credit scoring.
In this article, we present a novel methodology to assess predictive models for a binary target. In our opinion, the main weakness of the criteria proposed in the literature is not to take the financial costs of a wrong decision into account.
The objective of this article is to derive the optimal cut-off in predictive classification models and to improve model assessment on the basis of a general class of loss functions. We describe how our proposal performs in a real application on credit scoring.
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