MetOffice / MetOffice/XBTs_classification
Use auto ML libraries with XBT data
- Dominant language
- Jupyter Notebook
- Stars
- 4
- Forks
- 2
- PR merge metrics
- No merged PRs in 30d
Description
See how various auto ML packages do with the XBT data. These could include:
Azure ML https://ml.azure.com/
AWS sage maker https://aws.amazon.com/sagemaker/
Auto sk learn https://automl.github.io/auto-sklearn/master/
TPOT https://epistasislab.github.io/tpot/?spm=a2c65.11461447.0.0.68b37903PPlDLk
Contributor guide
No contributing guide indexed for this repository
Research direction
No files, tests, entry points, evaluation criteria, or expected outputs are named. Start by reviewing the XBT data and the linked Azure ML, AWS SageMaker, auto-sklearn, and TPOT resources. Before implementation, establish which packages and comparisons are in scope and what result would count as done.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- aws, azure, jupyter-notebook, machine-learning
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100