- 889 Views
- 2 replies
- 0 kudos
For tuning hyperparameters with Apache Spark ML / MLlib, when should I use Spark ML's built-in tuning algorithms vs. Hyperopt?
When should I use Spark ML's CrossValidator or TrainValidationSplit, vs. a separate tuning tool such as Hyperopt?
- 889 Views
- 2 replies
- 0 kudos
Latest Reply
- 0 kudos
Both are valid choices. By default, I'd recommend using Hyperopt nowadays. Here's the rationale, as pros & cons of each.Spark ML's built-in toolsPros: These fit the Spark ML Pipeline framework, so you can keep using the same type of APIs.Cons: Thes...
- 0 kudos