jaberg99

3 ms·

1. 1.▲
   by jaberg99 13y ago

   I filed this as a hyperopt ticket [1] My hunch is that this algorithm, like GP-based strategies will excel in small numbers of dimensions, but struggle in high numbers of dimensions, especially with conditional parameters. But it's a c
2. 2.▲
   by jaberg99 13y ago

   The purpose of a hyperparameter opt algo is definitely to have fewer than the thing it is configuring, and definitely for it to be faster to get a good model by just running the default search policy than by trying to rig up your own meta-a
3. 3.▲
   by jaberg99 13y ago

   Different configuration search strategies will perform differently on different fitness landscapes, and fitness landscapes depend on both data and learning algorithm. An online bandit-style selection of what seems to be working could be a g
4. 4.▲
   by jaberg99 13y ago

   Thanks for the HN post @chewxy. I just wanted to mention a bit of progress status. The version on pypi has been a workhorse. We're currently working on a not-so-minor update that will include a major upgrade in code quality, better do
5. 5.▲
   by jaberg99 13y ago

   Hey, hyperopt author here. I started something of a spearmint binding [1] but I didn't get very far yet. The main trouble is that spearmint currently does not have proper support for parameters that apply "sometimes". There&#