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A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification
| Content Provider | MDPI |
|---|---|
| Author | Michelucci, Umberto Sperti, Michela Piga, Dario Venturini, Francesca Deriu, Marco A. |
| Copyright Year | 2021 |
| Description | This paper presents the intrinsic limit determination algorithm (ILD Algorithm), a novel technique to determine the best possible performance, measured in terms of the AUC (area under the ROC curve) and accuracy, that can be obtained from a specific dataset in a binary classification problem with categorical features regardless of the model used. This limit, namely, the Bayes error, is completely independent of any model used and describes an intrinsic property of the dataset. The ILD algorithm thus provides important information regarding the prediction limits of any binary classification algorithm when applied to the considered dataset. In this paper, the algorithm is described in detail, its entire mathematical framework is presented and the pseudocode is given to facilitate its implementation. Finally, an example with a real dataset is given. |
| Starting Page | 301 |
| e-ISSN | 19994893 |
| DOI | 10.3390/a14110301 |
| Journal | Algorithms |
| Issue Number | 11 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-10-20 |
| Access Restriction | Open |
| Subject Keyword | Algorithms Operations Research and Management Science Machine Learning Intrinsic Limits Roc Curve Binary Classification Area Under the Curve Naïve Bayes Classifier |
| Content Type | Text |
| Resource Type | Article |
