Name
Abstract | ||
|---|---|---|
In practical applications of machine learning, it is necessary to look beyond standard metrics such as test accuracy in order to validate various qualitative properties of a model. Partial dependence plots (PDP), including instance-specific PDPs (i.e., ICE plots), have been widely used as a visual tool to understand or validate a model. Yet, current PDPs suffer from two main drawbacks: (1) a user must manually sort or select interesting plots, and (2) PDPs are usually limited to plots along a single feature. To address these drawbacks, we formalize a method for automating the selection of interesting PDPs and extend PDPs beyond showing single features to show the model response along arbitrary directions, for example in raw feature space or a latent space arising from some generative model. We demonstrate the usefulness of our automated dependence plots (ADP) across multiple use-cases and datasets including model selection, bias detection, understanding out-of-sample behavior, and exploring the latent space of a generative model. The code is available at . |
Year | Venue | DocType |
|---|---|---|
2020 | UAI | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David I. Inouye | 1 | 1 | 1.71 |
| Liu Leqi | 2 | 0 | 0.68 |
| Joon Sik Kim | 3 | 3 | 2.40 |
| Bryon Aragam | 4 | 24 | 6.89 |
| Pradeep D. Ravikumar | 5 | 2185 | 155.99 |