measure: Logarithmic loss
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- Dominant language
- PHP
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- 755
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Description
I would like to get measures out of the OpenML database (I am working with the snapshot).
Task: Classification
I am interested in error rate (1-predictive.accuracy), auc (area.under.roc.curve), brier score (root.mean.squared.error^2). Logarithmic loss is not accessible, is it?
Contributor guide
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
No file, test, or entry point is named. Start by tracing how classification measures are represented and exported in the OpenML snapshot, then compare the existing error rate, AUC, and Brier score fields with logarithmic loss. Done means determining whether the measure exists and, if absent, what exposure or implementation is required.
Written by the indexing model from the issue text.
Assessment
- Domain
- databases, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100