Ideas for testing
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Description
This issue suggests some ideas, which you might use to improve your testing. Normally writing tests can be a very time-consuming task and it is crucial to have a large number of tests for good coverage.
- check failure for invalid hyper-parameters
- check the construction of a model with a well-known dataset
- check that linearly separable data has an optimal accuracy
- check special qualities of your algorithm, e.g. can detect/is robust against outliers, construct sparse solutions etc.
- use a real-world dataset and compare to performance with similar implementations
- look into scikit-learn and how they are performing the testing
If you have any specific test idea for any algorithm in the linfa ecosystem, please add a comment below 🎉
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the existing tests across the linfa algorithms and compare their coverage with scikit-learn's testing practices. Define a focused set of algorithm-specific tests for invalid hyper-parameters, known datasets, accuracy, robustness, or performance, with completion marked by implemented tests and passing results.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- rust, scikit-learn
- Domain
- machine-learning, testing
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100