Name
Abstract | ||
|---|---|---|
Lale is a sklearn-compatible library for automated machine learning (AutoML). It is open-source (https://github.com/ibm/lale) and addresses the need for gradual automation of machine learning as opposed to offering a black-box AutoML tool. Black-box AutoML tools are difficult to customize and thus restrict data scientists in leveraging their knowledge and intuition in the automation process. Lale is built on three principles: progressive disclosure, orthogonality, and least surprise. These enable a gradual approach offering a spectrum of usage patterns starting from total automation to controlling almost every aspect of AutoML. Lale provides compositional constructs that let data scientists control some aspects of their pipelines while leaving other aspects free to be searched automatically. This tutorial demonstrates the use of Lale for various machine-learning tasks, showing how to progressively exercise more customization. It also covers AutoML for advanced scenarios such as class imbalance correction, bias detection and mitigation, multi-objective optimization, and working with multi-table datasets. While Lale comes with hyperparameter specifications for 216 operators out-of-the-box, users can also add more operators of their own, and this tutorial covers how to do that. Overall, this tutorial teaches you how you can exercise fine-grained control over AutoML without having to be an AutoML expert. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3534678.3542630 | KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Hirzel | 1 | 704 | 47.87 |
| Kiran Kate | 2 | 0 | 1.69 |
| Parikshit Ram | 3 | 0 | 0.34 |
| Avraham Shinnar | 4 | 0 | 1.69 |
| Jason T. Tsay | 5 | 533 | 23.98 |