Name
Abstract | ||
|---|---|---|
A recent advancement in the machine learning community is the development of automated machine learning (autoML) systems, such as autoWeka or Googleu0027s Cloud AutoML, which automate the model selection and tuning process. However, while autoML tools give users access to arbitrarily complex models, they typically return those models with little context or explanation. Visual analytics can be helpful in giving a user of autoML insight into their data, and a more complete understanding of the models discovered by autoML, including differences between multiple models. In this work, we describe how visual analytics for automated model discovery differs from traditional visual analytics for machine learning. First, we propose an architecture based on an extension of existing visual analytics frameworks. Then we describe a prototype system Snowcat, developed according to the presented framework and architecture, that aids users in generating models for a diverse set of data and modeling tasks. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Human-Computer Interaction | Architecture,Computer science,Model selection,Visual analytics,Human–computer interaction,Cloud computing,Multiple Models |
DocType | Volume | Citations |
Journal | abs/1809.10782 | 0 |
PageRank | References | Authors |
0.34 | 13 | 12 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dylan Cashman | 1 | 10 | 3.11 |
| Shah Rukh Humayoun | 2 | 113 | 27.04 |
| Florian Heimerl | 3 | 252 | 15.26 |
| Kendall Park | 4 | 0 | 0.34 |
| Subhajit Das | 5 | 13 | 6.22 |
| John Thompson | 6 | 38 | 2.81 |
| Bahador Saket | 7 | 140 | 11.70 |
| Abigail Mosca | 8 | 0 | 0.34 |
| John Stasko | 9 | 5655 | 494.01 |
| Alex Endert | 10 | 974 | 52.18 |
| Michael Gleicher | 11 | 4378 | 351.49 |
| Remco Chang | 12 | 983 | 64.96 |