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[Feature Request] Manually configuring models and hyperparameter tuning

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
20/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Stale
Tech stack
python
Domain
cloud, machine-learning

Research direction

Start by reviewing the linked AutoGluon time-series documentation for manually configuring models and hyperparameter tuning, then inspect the repository's existing cloud forecasting entry points. The issue names no files or tests, so first determine where model configuration is exposed and how completion would be verified for both requested capabilities.

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Description

Hi, is there currently support for manually configuring models and hyperparameter tuning? If not I'd like to submit a feature request (probably lower priority IMHO than known_covariates).

Dominant language
Python
Stars
41
Forks
17
Avg merge
6h 56m
Merged PRs (30d)
28

Getting set up

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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