Name
Abstract | ||
|---|---|---|
We propose a general model explanation system (MES) for the output of black box classifiers. This paper describes extensions to Turner (2015), which is referred to frequently in the text. We use the motivating example of a classifier trained to detect fraud in a credit card transaction history. The key aspect is that we provide explanations applicable to a single prediction, rather than provide an interpretable set of parameters. We focus on explaining positive predictions (alerts). However, the presented methodology is symmetrically applicable to negative predictions. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Machine Learning | Black box (phreaking),Computer science,Credit card,Artificial intelligence,Database transaction,Classifier (linguistics),Machine learning |
DocType | Volume | Citations |
Journal | abs/1606.09517 | 3 |
PageRank | References | Authors |
0.46 | 2 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Turner, Ryan D. | 1 | 34 | 4.33 |